Staphylococcus aureus infected rabbit tibia osteomyelitis. Significantly, MV‐excited MCT and MDT of CNT‐CuHHTP developed in this study makes a major step forward in antibiotic‐free MV therapy in deep tissue bacterial infection diseases.Microwave (MV)‐trigged dynamic therapy based on MV‐responsive materials is promising for treating deep infection diseases that cannot be effectively treated by antibiotics, like life‐threatening osteomyelitis. Surface states of materials affect the generation of free charges under the excitation source with energy less than the band gap, consequently influencing the MV dynamic effects. Herein, an MV responsive system with interface confined 2D metal–organic framework (2D MOF) on oxidized carbon nanotube (CNT) is prepared, in which the ultrasmall Cu‐based 2D MOF possesses sufficient surface/interface defects, endowing the system a large number of surface states. Under MV irradiation, the synthesized CNT‐2D MOF not only efficiently absorbs and converts the microwave into heat for microwaveocaloric therapy (MCT) via enhanced hetero‐interfacial polarization, but also generates excited electrons via surface state for microwave dynamic therapy (MDT). This biocompatible CNT‐2D MOF exhibits highly effective broad‐spectrum antimicrobial activity against seven pathogenic bacteria, including Gram‐negative and Gram‐positive pathogens, under 7 min MV irradiation. And this system is proven to efficiently eradicate  Staphylococcus aureus‐infected osteomyelitis by synergistic microwave thermotherapy and microwave kinetic therapy.Antibiotic‐free deep antibacterial strategy. CNT‐CuHHTP designed through interface confinement engineering exhibits broad‐spectrum antibacterial properties under microwave irradiation and can eliminate  Clinical treatment always requires multiple invasive surgeries and long‐term antibiotic therapy, which brings not only pains to patients, but also serious side effects and even drug resistance caused by antibiotics. In addition, an antibiotic‐free therapy, phototherapy, including photothermal therapy and photodynamic therapy, is ineffective for the treatment of deep tissue infections limited by the poor penetration of light. Hence, it is urgent to develop an non‐invasive antibiotic‐free strategy for deep tissue infections.Deep tissue infection diseases, for example, osteomyelitis, induced by pathogenic bacteria has threatened human health.1 With Microwave sensitizer makes a main role on absorbing and converting the MV into microwaveocaloric via the dielectric loss or magnetic loss for improved MCT. Oxidized carbon nanotube (CNT) with large aspect ratio is easy to form a conductive network structure, which is conducive to the absorption mechanism of multiple reflections. Moreover, the large specific surface area and small size effect of CNT increase the crystal defects and dangling bonds due to the high proportion of surface atoms, which is conducive to the increase of dielectric loss due to dipole polarization. However, the incident MV is severely reflected and cannot be converted into heat for MCT, when CNT is used alone due to the skin effect caused by its high conductivity. Coating a layer of material with moderate conductivity on the surface of CNT can effectively adjust the wave resistance matching by heterointerfacial polarization while maintaining the high dielectric loss and network‐like structure, which are conducive to efficient MCT. In addition, unlike most MOFs with poor conductivity, the metal ions in CuHHTP and aromatic organic ligands will form abundant π–π/π–d conjugated structures when they are bonded, presenting delocalized electrons similar to graphene, thus producing high conductivity. Moreover, CuHHTP usually contains a lot of micropores and has a large specific surface area, which increases the active sites of the reaction to a certain extent. As a 2D layered conductive metal–organic framework, the conductivity of CuHHTP is much weaker than CNT, making this 2D MOF avoid the skin effect. In addition, CuHHTP is composed of copper atoms and 2,3,6,7,10,11‐hexahydroxytriphenylene (HHTP) ligands, its intrinsically porous structure is conducive to the absorption of MV. Furthermore, the organic–inorganic hybrid structure commonly leads to a large number of unsaturated sites  and many dangling bonds , which are beneficial to the dipole polarization consumption of MV for improved MCT.Microwave (MV), as an electromagnetic wave with a longer wavelength than light, has a strong penetration ability and is a promising exogenous energy source to assist treatment of deep tissue infection diseases. Recently, microwaveocaloric therapy (MCT) with high heating efficiency and deep tissue penetration ability has been widely used in clinical practice.5 Micr−3 eV, which is not enough to activate bacterial‐toxic reactive oxygen species (ROS) generation similar to photodynamic therapy in theory. In practice, there are a wide variety of surface states existing in the forbidden band of the semiconductor, due to the surface effect including surface steps, unsaturated bonds, and adsorbed atoms/molecules on the surface of the material. These surface states correspond to different surface energy levels, with multi‐level characteristics distributed in the forbidden band, and the energy levels matching the microwave energy may appear in the multi‐level status.On the contrary, the development of microwave dynamic therapy (MDT) has been limited because the MV energy is only 10n theory.11 In 2 centers), which is the key to generate surface states on its surface. Meanwhile, the oxygen‐containing functional groups on CNT provide nucleation sites that allow 2D MOF to nucleate and then grow into MOF crystals. Because of the steric hindrance effect, the epitaxial growth of CuHHTP will be affected. Therefore, when CNT is combined with CuHHTP, CNT will limit the infinite epitaxial growth of CuHHTP, so that the CuHHTP sheet smaller and has more surface states, which is conducive to efficient microwave dynamic therapy.Therefore, we speculate that these surface energy levels could alleviate the hopping difficulty of electrons through multi‐level transitions, which will promote the generation of excited electrons under microwave stimulation and achieve an enhanced MDT. The above mentioned CuHHTP commonly possesses unsaturated bonds  of CNT could absorb the free Cun Figure 1a. Insten Figure  and the n Figure , inset. n Figure , revealin Figure  and lattn Figure . Additio2.2Figure θ = 26°, which is assigned to the (002) plane of the graphite‐like structure of CNT, indicating the coexistence of CuHHTP and CNT in the CNT‐CuHHTP. Especially, the position of CuHHTP (022) peak after CNT doping moves from 28.0° to 27.7°, and the corresponding lattice spacing increases from 0.309 to 0.312 nm. This is mainly due to the lattice distortion of ultrasmall CuHHTP caused by the confinement effect. Besides, the X‐ray photoelectron spectroscopy (XPS) full spectrum  binding energy was observed in CNT‐CuHHTP compared to the one in CuHHTP, further suggesting a strong chemical interaction between CuHHTP and CNT. These results illustrate the successful construction of CNT‐CuHHTP heterostructure.The X‐ray diffraction (XRD) pattern of the CNT‐CuHHTP Figure 2a exhibi0 and oxidized Cu2+  oscillation curve of CNT‐CuHHTP at Cu K‐edge shows a different trend in shape and oscillating frequency to that of control samples over the whole range of 0–12 Å−1  contour plots of Cu foil displayed the maximum intensity at 7.0 Å−1  and Cu  was performed. The Cu K‐edge shows the absorption edge positions of copper in CNT‐CuHHTP is between Cu foil and CuO references, means that the average valance state of copper in CNT‐CuHHTP is between metallic Cu+ Figure , which i1 Figure  is contrVp) of the sample is determined by its reconstructed phase shift (Δθ) and thickness (t), where CE is the interaction constant (0.00728 rad V−1 nm−1 for 200 kV accelerating voltage). Following this method, a representative electron hologram of CNT‐CuHHTP (sample) is shown in field of view with a vacuum region . As shown in Figure −2, the saline solution of CNT‐CuHHTP can even reach up to 68 °C after 7 min MV irradiation  and CNT (0.07), indicating that CNT‐CuHHTP absorbed more incident MV than CuHHTP or CNT  value, where a lower RL value means stronger MV absorption and more efficient energy conversion capacity.14 CNTs Figure . Specifis Figure . The aboT Figure . Besidest Figure . Althougε″) and imaginary magnetic loss (µ″), which represent the ability to dissipate electromagnetic energy. Specifically, CNT‐CuHHTP with suitable dielectric loss  of CNT‐CuHHTP was obviously stronger than that of CNT, demonstrating the more effective charge transfer in the CNT‐CuHHTP heterointerface under MV irradiation. Notably, the MV‐current of CNT‐CuHHTP has shrilly front‐peak and back‐peak, which is consistent with the characteristics of surface states in the photocurrent, indicating CNT‐CuHHTP contains many surface states and defects, which are favorable for multi‐level transition of electron under the excitation of MV. In addition, the MV‐current density of CNT‐CuHHTP under different MV pulse conditions were almost similar, while the MV‐current also exhibited the obvious front‐peak and back‐peak  . As shown in ocurrent,17 indk Figure , indicatk Figure . It indik Figure  of CuHHTk Figure , which f− Figure , not hydEg) demonstrated that the bandgap of CuHHTP is 1.05 eV. Evidently, the energy of the MV (10−3 eV) is not enough to excite the bulk of CNT‐CuHHTP to generate charge carriers for ·O2− (O2 + e−→·O2−). It is further demonstrated that the CNT‐CuHHTP has no bulk interband transition  to the conduction band (Ec) under MV irradiation), and the generation of carriers mainly depends on the surface states. Further, we determined the workfunction (Φ) of the samples by ultraviolet photoelectron spectroscopy (UPS). Specifically, the measured secondary electron cutoff energy of CNT, CuHHTP, and CNT‐CuHHTP were 16.81 eV, 16.65 eV, and 16.44 eV, respectively . The distance between Ev and Fermi level (Ef) of CuHHTP and CNT‐CuHHTP were 5.17 eV and 5.52 eV, respectively.According to the UV−vis diffuse reflectance spectra  site of CuHHTP and CNT‐CuHHTP is more likely to adsorb oxygen  is formed, and the direction of the electric field is from the CNT side to the CuHHTP, that is, the CNT side has a positive charge and the CuHHTP side has a negative charge, which is consistent with the result of electron hologram charge distribution. Moreover, the built‐in electric field can drive electrons rapidly transferred from CuHHTP to CNT. As a result, under MV irradiation, MV can excite surface energy levels matching their energy to generate more MV‐excited electrons, which are transferred to CNT driven by a built‐in electric field to reduce recombination. These free electrons react with oxygen to generate ·O2− for eradication of bacteria.The MV‐dynamic mechanism of CNT‐CuHHTP is summarized in Figure 18 Aft2.5−) and Gram‐positive (G+) bacteria. It can be easily observed that the number of E. coli after CNT‐CuHHTP synergistic MV (CNT‐CuHHTP+MV) treatment for only 7 min was significantly reduced compared with other groups , Salmonella typhimurium (S. typhimurium), Aeromonas veronii (A. veronii), and Proteus vulgaris (P. vulgaris), and G+ bacteria methicillin‐resistant S. aureus (MRSA). In addition, the antibacterial effects of different treatment groups against different bacteria are shown in Figures − bacteria the group of CNT‐CuHHTP had low antibacterial rates of 51.86% against E. coli EPEC, 51.69% against S. typhimurium, 88.59% against A. veronii, and 61.67% against P. vulgaris, respectively. Comparatively, the CNT‐CuHHTP+MV group showed higher bacterial‐killing efficiency against E. coli EPEC (95.63%), S. typhimurium (99.83%), A. veronii (100%), and P. vulgaris (99.78%). Similarly, for the antibacterial rate of CNT‐CuHHTP+MV group against G+ bacteria is higher than that of CuHHTP group, although its antibacterial rate against MRSA is only 48.54%. Notably, the antibacterial rate of CNT‐CuHHTP+MV group against S. aureus and MRSA can reach 99.93% and 96.77%, respectively, by increasing the MV power  is higher than that against G+ bacteria (82.43% for S. aureus and 48.5% for MRSA) under the same antibacterial conditions.We tested the synergistic antibacterial effect of CNT‐CuHHTP with MV in vitro, including against typical Gram‐negative  and after CNT‐CuHHTP+MV treatment by atomic force microscopy (AFM). Specifically, E. coli in the Ctrl group were rod‐like shape and plumply cell  showed the outline of the bacterial membrane without the green fluorescent signal  can release 11.56 ppm copper ion in 48 h. After MV irradiation for 7 min, the concentration of copper ion can reach 10.18 ppm  on the viability of A549. As show in Figure Figure −1) was only 6.3%  in the bone marrow in the CNT‐CuHHTP+MV group was 0.619, which was also the least compared to the Ctrl (7.181), CNT‐CuHHTP (4.635), and MV (2.277) groups  reached the normal level. A similar trend emerged at after 5 days of treatment. To evaluate the inflammatory response of different treatment groups the levels of TNF‐α and IL‐6 were measured. The CNT‐CuHHTP+MV group had the lowest concentrations of TNF‐α , diffuses distribution of inflammatory cells in the liver and spleen (indicated by yellow), and alveolar epithelial hyperplasia with red mucus (indicated by black arrows). These symptoms almost disappeared in group of CNT‐CuHHTP+MV, demonstrating the excellent antibacterial properties of MCT and MDT in vivo. Notably, the improvement effect of CNT‐CuHHTP+MV on osteomyelitis is comparable to that of traditional antibiotics gentamicin (Gent)  and two Gram‐positive bacteria (S. aureus and MRSA) with only 7 min of MV irradiation. Its robust broad‐spectrum antibacterial effectiveness is mainly attributed to the destruction of bacterial membranes caused by microwaveocaloric and microwave dynamics effects (·O2−). Specifically, the efficient microwaveocaloric effect of CNT‐CuHHTP is own to the reasonable wave‐resistance matching and attenuation constant caused by multiple reflections, interface polarization, dielectric loss and dipole polarization. Besides, under microwave irradiation, CNT‐CuHHTP generates excited electrons through the surface energy level, and the built‐in electric field accelerates the transfer of excited electrons to CNT and promotes the generation of ·O2−. The in vivo results confirm that this antibiotic‐free microwave therapy can cure osteomyelitis by reducing bacterial counts with topical treatments. This platform may bring further insight and understanding of deep tissue infection diseases treatment through antibiotic‐free microwave therapy.CNT confined the epitaxial growth of CuHHTP, resulting in the formation of ultra‐small CuHHTP growing along the CNT skeleton, accompanied by abundant surface states which were mainly derived from the surface/interface defects  of CNT‐CuHHTP. CNT‐CuHHTP can efficiently kill five Gram‐negative bacteria (43)2·3H2O) was purchased from Aladdin. HHTP was purchased from Meryer (China). SYTOX Green Ready Flow Reagent (R37168), and FM 4–64 (T13320) were purchased from Thermo Fisher scientific (USA). The rabbit Interleukin‐6 ELISA Kit (CK‐E72064) and rabbit tumor necrosis factor alpha (TNF‐α) ELISA Kit (CK‐E72292) were purchased from Nanjing Herbal Source Biotechnology Co., Ltd. (China).Multi‐walled CNT was purchased from Aiweixin Chemical Technology Co., Ltd. . Copper nitrate trihydrate  was dispersed by ultrasound in 4 mL water, and the aqueous solution (4 mL) of copper acetate (30 mg) and CNT (10 mg) mixed by ultrasound was added slowly. The mixture was sonicated for 15 min and the blue solution was obtained. Subsequently, the blue solution was transferred to the glass sample bottle and heated at 85 °C for 24 h. The CNT‐CuHHTP was obtained after centrifuged and washed with water and acetone, respectively. The obtained CNT‐CuHHTP was dried under vacuum. The same preparation process was used to obtain CuHHTP, except that no CNT was added during the reaction process.The TEM image and elemental mapping images of CNT‐CuHHTP were observed using TEM (JEM‐2100F). The HAADF‐STEM image was observed using JEOL JEM‐ARM200F. The HRTEM images and off‐axis electron holography measurements were performed using image spherical aberration corrected transmission electron microscopy (Image Cs‐Corrected TEM) (FEI‐Themis Z). The phase reconstruction was processed by a dedicated script in the Digital Micrograph software package (Gatan Inc.) based on the fast Fourier transform (FFT) algorithm on the image spherical aberration corrected transmission electron microscopy (Image Cs‐Corrected TEM) (FEI‐Themis Z). The crystal structures and surface compositions were collected with XRD  and XPS . Cu K‐edge XAFS analyses were performed with Si (111) crystal monochromators at the BL14W beamline at the Shanghai Synchrotron Radiation Facility (SSRF) . Before the analysis at the beamline, samples were placed into aluminum sample holders and sealed using Kapton tape film. The XAFS spectra were recorded at room temperature using a 4‐channel Silicon Drift Detector (SDD) Bruker 5040. The spectra was processed and analyzed by the software codes Athena.−1) was dispersed by ultrasound in a 2 mL Eppendorf (EP) tube. Then the EP tube with CNT‐CuHHTP solution (1 mL) was irradiated by microwave therapy instrument  at 2.45 GHz for 7 min. The temperature of the solution was recorded every minute using an FLIR thermal camera . Similarly, the microwaveocaloric temperatures of CNT and CuHHTP in saline solution were also collected by this method.The saline solution of CNT‐CuHHTP (1 mg mL−1) in 2 mL EP tube was placed on pork with different thicknesses  and applied MV for 7 min across the biological tissues.Subsequently, infrared thermal images were also obtained by FLIR thermal camera. The CNT‐CuHHTP solution . The MV absorption performances of CNT, CuHHTP, and CNT‐CuHHTP were evaluated by calculating the RL based on the transmission line theory.α was calculated as the following Equation (The attenuation constant Equation :(4)α=2SO4 (0.5 m) aqueous solution was built using a Pt plate as counter electrode, an Ag/AgCl electrode as reference electrode, and ITO conductive glasses (1 cm × 1 cm) with CNT or CNT‐CuHHTP as working electrode. UV–vis diffuse reflectance spectra were recorded by UV–vis spectrophotometer . Photoluminescence spectrofluorometer  was conducted with an excitation wavelength of 400 nm.MV‐electrochemical measurements were based on an electrochemical workstation (CHI 660E) equipped with 2.45 GHz microwave source. Moreover, a three‐electrode system in quartz glass cell in Na−1) under continuous MV irradiate were obtained using a ROS Assay Kit . Additionally,·O2− and ·OH were detect using 5,5‐dimethyl‐1‐pyrroline‐N‐oxide  as the trapping agent measured by electron paramagnetic resonance  spectrometer. Besides, 1O2 was using 2,2,6,6‐tetramethylpiperidine  as the trapping agent.The ROS amounts of the saline solutions of CNT, CuHHTP, and CNT‐CuHHTP  are calculated as Eads = Ead/sub − Ead – Esub, where Ead/sub, Ead, and Esub are the optimized adsorbate/substrate system, the adsorbate in the structure and the clean substrate, respectively.All of spin‐polarized calculations based on density functional theory (DFT) were performed by utilizing DMol3 package. The generalized gradient approximation (GGA) in the Perdew–Burke–Ernzerhof form and Semicore Pseudopotential method (DSPP) with the double numerical basis sets plus the polarization functional (DNP) were adopted. The Brillouin zones were sampled by using Monkhorst–Pack scheme with a 1×1×1k‐point grid and the double numerical basis sets plus the polarization functional (DNP) were adopted. A DFT‐D correction with Grimme scheme was used to account for the dispersion interaction. The SCF convergence for each electronic energy was set as 1.0 × 10E. coli (ATCC 8099), E. coli EPEC (CICC 10663), S. typhimurium (CCTCC PB 2019001), A. veronii (ATCC 35624), P. vulgaris (CCTCC AB 91103), S. aureus (ATCC 25923), and MRSA (CCTCC 16465) were quantitatively using the spread plate method. The bacterial suspensions were diluted to about 107 CFU mL−1 for antibacterial experiments. Subsequently, the diluted bacterial suspensions were incubated with physiological saline (Ctrl) and CNT‐CuHHTP (1 mg mL−1) in 2 mL EP tubes with and without MV irradiation for 7 min. After different treatments, the appropriate diluted bacterial solution was smeared on LB agar plate by coater and cultured at 37 °C. The colonies were counted after cultured 16–20 h to calculate the antibacterial ratio, which was then assessed using Equation (A and B represent the numbers of bacteria in the control group (Ctrl) and experimental group, respectively.The antibacterial test of CNT‐CuHHTP with or without MV against ii ATCC 3624, P. vE. coli and S. aureus before and after treatment with CNT‐CuHHTP+MV were measured by atomic force microscopy . In addition, the morphologies of the other bacteria  interacting with CNT‐CuHHTP with or without MV were evaluated using SEM  after fixation and dehydration.The morphologies of ydration.23E. coli stock solution was washed three times by centrifugation with normal saline, and then diluted to obtain a suspension with a concentration of 107 CFU mL−1. After different treatments, SYTOX Green (5 µm) was added and incubated for 4 min at room temperature. Then, FM4‐64 (5 ppm) was added and incubated for 1 min (on ice). Subsequently, the two stained cultures were centrifuged at 6000 × g for 30 s (4 °C) and resuspended in normal saline (1/10 volume). The resuspension solution (5 µL) was taken on a glass slide and covered with a coverslip to be photographed by a laser scanning confocal microscope .The α‐MEM) respectively, including supplemented with 10% v/v fetal bovine serum, 1% amphotericin, and 1% penicillin–streptomycin solution, and incubated at 37 °C containing with 5% CO2.The MC3T3‐E1 (ATCC CRL‐2593) and A549 (ATCC CCL‐185) were cultured in Dulbecco's modified eagle medium and minimum essential medium alpha  were co‐cultured with MC3T3‐E1 cells (105 cells/well). The Ctrl group was used the same volume of physiological saline instead of CNT‐CuHHTP. Subsequently, the cell viability was obtained using a MTT ‐2,5‐diphenyltetrazolium bromide) cell proliferation and cytotoxicity assay kit .The various concentrations of CNT‐CuHHTP (1 mg mL−1). The treated cells were cultured for three days. After incubation, the samples were washed with PBS, soaked in formaldehyde (4%) for 10 min, and rinsed with sterile PBS. These samples were then stained with isothiocyanate‐phalloidin (FITC) for 30 min and 4,6‐Diamidino‐2‐phenylindole (DAPI) for 20 s in the dark. Subsequently, the morphology of samples after rinsing with PBS were photographed using laser scanning confocal microscopy . In the experiment of evaluating the effect of CNT‐CuHHTP+MV treatment on A549, the MV  was irradiated after adding CNT‐CuHHTP, and the subsequent staining method was consistent with the above steps.For the fluorescence morphology, MC3T3‐E1 or A549 cells were first incubated in six‐well plates for 24 h and further incubated with/without CNT‐CuHHTP (1 mg mLg) for 5 min to obtain red blood cells (RBCs). Subsequently, the collected RBCs were resuspended using 10 mL PBS. Different concentrations of CNT‐CuHHTP were incubated with the diluted RBCs for four hours at 37 °C. After co‐cultured and centrifugated, the absorbance of the supernatant was measured at 405 nm. The positive control (Hemolytic efficiency was 100%) was used with 1% TritonX‐100 instead of CNT‐CuHHTP. The hemolysis percentage was calculated by comparing the absorbance values of these samples with the positive control.Evaluation hemolysis of CNT‐CuHHTP. 5 mL of New Zealand rabbit blood was diluted with PBS (50 mL) centrifuged (176 × −1) was injected in situ. After five days of operation, the blood routine, renal function, and hepatic function were measured. And histological analysis of major organs  was carried out.After drilled the hind legs of male New Zealand rabbits, 200 µL of CNT‐CuHHTP  Co., Ltd. . Male New Zealand rabbits  were randomly divided into four groups: the group of physiological saline (Ctrl), the treatment group of CNT‐CuHHTP (CNT‐CuHHTP), the treatment group of MV, the collaborative treatment group of CNT‐CuHHTP and MV (CNT‐CuHHTP +MV), and positive control antibiotic group (Gent).−1) prior to surgery. Then, hind legs were shaved and disinfected (iodophor) at the tibia and femur. After that, a hole was drilled in the tibial plateau by medical electric drill with a drill bit diameter of 2 mm. Subsequently, the legs were injected with 106 CFU (50 µL) of S. aureus suspension via this hole to establish the S. aureus‐infected osteomyelitis model. Physiological saline, Gent or CNT‐CuHHTP was added for different treatment options. Finally, the holes were sealed with bone wax and the muscles and skin were sutured. The rabbits were subjected to different treatments: The Ctrl group was injected with physiological saline (200 µL). The CNT‐CuHHTP group was injected with CNT‐CuHHTP . The Gent group was injected with Gent (3 mg kg−1). The MV group was injected with physiological saline (200 µL), and then irradiated MV  for 7 min; and the CNT‐CuHHTP + MV group was injected with 200 µL of CNT‐CuHHTP (5 mg mL−1) and irradiated with MV for 7 min.The rabbits were raised for 3 days in a 25 ± 2 °C and 60–70% (humidity) conditions. In brief, they were anaesthetized with an intramuscular injection of pentobarbital  and H&E staining (Day 2 and 5). After five days of treatment, the major organs were stained with H&E. The bone marrow was quantitatively diluted to counted the number of S. aureus colonies by spread‐plate method.After 5 days of treatment, the hind legs of the infected rabbits were examined by Animal Magnetic Resonance Imaging Platform  At a preset time, the blood of the rabbits were collected for INF‐t‐test was used for two‐group comparison and one‐ or two‐way analysis of variance (ANOVA) for multiple comparisons (GraphPad Prism 8.0 software). Significant differences are indicated by p‐values of less than 0.05. n.s. presents no significant difference.Each experiment contained at least three parallel samples and presented as mean ± standard deviation. A The authors declare no conflict of interest.Supporting InformationClick here for additional data file.


COVID-19 vaccine preferences can influence vaccine coverage. Discrete choice experiments (DCE) can be used to elicit people’s trade-offs. DCEs require evidence-based attribute selection and validation of understanding with lay audiences. To inform a future DCE, a pre-test was conducted to examine the survey and refine six attributes selected from a targeted literature review and expert interviews.Interviews were conducted in March 2023 in Canada, Germany, the UK, and US. Self-reported anti-vaccinationists were excluded. Eligible individuals were interviewed during the completion of a survey that included 11 choice tasks and supplementary questions. The “think aloud” method was used to evaluate participants’ understanding of the survey and if they were making trade-offs as hypothesized. Four country-level experts validated the survey modifications based on the results.Six phone interviews were completed in each country (N=24). Mean age was 43.7; 50% were women; 50% reported receiving the full COVID-19 vaccine series; 45.8% received the initial series but were unsure about additional doses; 1 was unvaccinated (4.2%).Participants’ top four priorities were vaccine protection against COVID-19, serious side-effects, protection against severe COVID-19, and common side-effects, followed by vaccine type and timing of COVID-19/influenza vaccines .More than half of the participants would consider co-administration of COVID-19 and influenza vaccines, either as two separate injections (58.3%) or as a single, combined injection (62.5%) . Most individuals (54.2%) preferred an annual COVID-19 vaccine; over every 6 months (4.2%), and 20.8% were indifferent .When deciding to get vaccinated, most considered the following to be important: how long a vaccine was examined in humans (65.2%), how long a vaccine was used in a vaccination program (62.5%); 50% considered vaccine type (mRNA or protein subunit) important .This study validated the importance of key vaccine attributes driving people’s choices and feedback was used to improve the clarity of attribute descriptions. A future DCE will be fielded to increase the understanding of COVID-19 vaccine preference and hesitancy.Sumitra Sri Bhashyam, MSc, PhD, Novavax Inc: Grant/Research Support L.G Shane, Pharm.D., RPH., BScPharm., Novavax Inc: Employee of Novavax Inc|Novavax Inc: Stocks/Bonds Hannah B. Lewis, MSc, PhD, Novavax Inc: Grant/Research Support Marie de la Cruz, MS, Novavax Inc: Grant/Research Support Jayne Galinsky, PhD, Novavax Inc: Grant/Research Support Keeva Demchuk, n/a, Novavax Inc: Grant/Research Support Nancy M. Waite, Waite PharmD FCCP, GSK: Advisor/Consultant|Novavax Inc: Honoraria|Pfizer: Advisor/Consultant|Sanofi: Advisor/Consultant|Sanofi: Grant/Research Support Jeffrey V. Lazarus, PhD, MIH, MA, AbbVie: Advisor/Consultant|AbbVie: Conference travel|Gilead Sciences: Advisor/Consultant|Gilead Sciences: Grant/Research Support|Gilead Sciences: Honoraria|Moderna: Honoraria|Novavax Inc: Advisor/Consultant|Novavax Inc: Honoraria|Novo Nordisk: Honoraria|Roche Diagnostics: Grant/Research Support David M. Salisbury, CB FMedSci FRCP FRCPCH FFPH, Clover Pharmaceuticals: Advisor/Consultant|GSK: Advisor/Consultant|Moderna: Advisor/Consultant|Novavax Inc: Honoraria|Sanofi: Advisor/Consultant


Objective: We aim to compare the capabilities of Chat Generative Pre-Trained Transformer (ChatGPT)-3.5 and ChatGPT-4.0  in addressing multiple-choice ophthalmic case challenges.Methods and analysis: Both models’ accuracy was compared across different ophthalmology subspecialties using multiple-choice ophthalmic clinical cases provided by the American Academy of Ophthalmology (AAO) “Diagnosis This” questions. Additional analysis was based on image content, question difficulty, character length of models’ responses, and model’s alignment with responses from human respondents. χ2 test, Fisher’s exact test, Student’s t-test, and one-way analysis of variance (ANOVA) were conducted where appropriate, with p<0.05 considered significant.Results: GPT-4.0 significantly outperformed GPT-3.5 , with the most noticeable improvement in neuro-ophthalmology . While both models struggled with uveitis and refractive questions, GPT-4.0 excelled in other areas, such as pediatric questions (82%). In image-related questions, GPT-4.0 also displayed superior accuracy that trended toward significance . GPT-4.0 performed better with easier questions (93.8% (least difficult) versus 76.2% (middle) versus 53.3% (most), p=0.03) and generated more concise answers than GPT-3.5 . Moreover, GPT-4.0’s answers were more in line with those of AAO respondents , showing a strong correlation between its accuracy and the proportion of AAO respondents who selected GPT-4.0’s answer .Conclusion and relevance: Our study demonstrated that GPT-4.0 significantly outperforms GPT-3.5 in addressing ophthalmic case challenges, especially in neuro-ophthalmology, with improved accuracy even in image-related questions. These findings underscore the potential of advancing artificial intelligence (AI) models in enhancing ophthalmic diagnostics and medical education. The intersection of artificial intelligence (AI) and healthcare presents opportunities for improved clinical decision-making and medical education. The rising competency of AI in diagnosing diverse ocular conditions underscores its potential within ophthalmology ,2. In thSince then, the GPT model has been updated from GPT-3.5 to GPT-4.0, showcasing potential enhancements in AI-enabled medical education. The new model developed better contextual understanding, enhanced topic consistency, and markedly increased accuracy as evidenced by its success on professional and academic assessments, improving from the 10th percentile to the 90th percentile on the Uniform Bar Exam . Recent Clinical ophthalmology questions were sourced from the 2022 release of the American Academy of Ophthalmology (AAO) “Diagnose This,” a compendium of weekly clinical ophthalmology multiple-choice questions . We chosThe primary objective of the study was to compare the accuracy of GPT-3.5 and GPT-4.0 across different subspecialties of ophthalmology. The secondary objectives included evaluating performance on image-related questions, varying question difficulty, character length of model responses, and alignment with responses from AAO respondents.All 52 questions from the 2022 AAO “Diagnose This” were categorized based on the specified ophthalmology subspecialty: cornea/anterior segment, glaucoma, neuro-ophthalmology, ocular pathology/oncology, oculoplastic/orbit, pediatric, refractive, retina, and uveitis. Opening and utilizing a premium OpenAI account with no prior usage, we ensured zero preceding conversation history. Questions and corresponding choices were inputted directly into GPT-3.5 and GPT-4.0 without initial prompts. To maintain the integrity of responses, we cleared each prior ChatGPT conversation since the model’s replies can be shaped by ongoing dialogues. Example input and outputs can be referenced in the Results. Responses were assessed for accuracy by checking the selected answer from GPT outputs with AAO’s correct answer. Any ambiguity in model choices triggered a re-prompt, instructing them to “choose one of the following options.”For the secondary analysis, we aimed to analyze ChatGPT’s performance based on not only the subject but also the level of difficulty, type of question, and agreement with human respondents. As AAO cases lacked formal difficulty labels, we inferred based on the accuracy percentage of AAO respondents: least (≥80%), middle (79%-51%), and most difficult (≤50%). Questions referencing images were tagged as “image” cases. For these questions, GPT model inputs were aided with descriptions based on the image interpretations published in the AAO answer during an “image-aided” cycle. The “combination” runs combined raw text inputs for non-“image” queries with the “image-aided” input. Further analysis was conducted on the complexity of GPT model responses by examining the verbosity of the models’ responses based on output character length. We sought to determine if there was any correlation between the output length and the inherent difficulty of the question. Moreover, we conducted a segment-based analysis, categorizing questions into those pertaining to the anterior segment of the eye and those related to the posterior segment. Additionally, we differentiated between purely textual questions and those that referenced or were based upon images. Lastly, to gauge the alignment of the GPT models with human responses, we quantified the proportion of AAO respondents’ selections that matched the GPT models’ choices.χ2 test, Fisher’s exact test, Student’s t-test, and one-way analysis of variance (ANOVA) were conducted where appropriate . The homGPT-4.0 performed significantly better than GPT-3.5  (Table Among the 30 (58%) image-related questions, GPT-4.0 demonstrated greater accuracy than GPT-3.5 . There was no notable difference between the two models in categorical performance. The performance gap between the models remained consistent when they were provided with image interpretations. GPT-3.5 improved its accuracy by 14%, while GPT-4.0 improved by 16%. When presented with a combination of text-based questions and image questions with interpretations, GPT-4.0 still outperformed GPT-3.5 by a large margin  with a significant improvement for neuro-ophthalmology cases (p=0.03). Examples of the GPT models’ inputs and outputs for the text-only and image questions can be found in Table When the questions were categorized by difficulty according to the percentage of AAO respondents who answered each correctly, GPT-3.5 did not show any difference in accuracy across difficulty levels (p=0.30), while GPT-4.0 performed better on easier questions relative to harder ones (94% (most difficult) versus 76% (middle) versus 53% (least), p=0.03) . GPT-3.5 had similar agreement rates regardless of question difficulty, while GPT-4.0 had higher agreement rates for easier questions and lower agreement rates for harder questions (83% (most difficult) versus 52% (middle) versus 37% (least), p<0.01). Additionally, there was a strong correlation between GPT-4.0’s accuracy and the proportion of AAO respondents who selected GPT-4.0’s answer . The agreement rates of both models did not vary by segment or question type.Our study highlights significant advancements in the application of AI for ophthalmology, particularly in the performance metrics between GPT-4.0 and GPT-3.5. Specifically, GPT-4.0 significantly outperformed GPT-3.5 in overall accuracy and in the neuro-ophthalmology subspecialty, as detailed in Table The effectiveness of NLP models in addressing AAO clinical scenarios is further evidenced by their improving track record on ophthalmic board practice questions ,9,12. MiOpenAI’s release of GPT-4.0 brought several improvements. When applied to specialized domains such as ophthalmology, these advancements lead to increased accuracy. One of the most notable changes is the sheer scale. The original GPT model had 117 million parameters, while GPT-3.0 had 175 billion parameters, and GPT-4.0 boasts a staggering 170 trillion . The masRegarding the conciseness of GPT-4.0’s answers, this attribute may have significant practical implications. With the widespread use of practice questions among medical and proven associations between the number of completed practice questions and board examination performance , the abiWhile the rapid evolution of the GPT models offers substantial benefits for medical training, there are risks of misinformation. As highlighted in Table While our methodology was designed to broadly assess GPT performance, the investigation possessed certain limitations. Most notably, the use of multiple-choice questions does not fully capture the intricate dynamics of clinical decision-making. Although these types of questions can test factual knowledge effectively, these questions oversimplify scenarios and only present the relevant parts of the presentation. In practice, physicians do not simply select from predefined options. They must consider a broad spectrum of symptoms, patient history, and individual factors that may affect potential outcomes. This depth and complexity of a patient necessitate an evaluation of the patient in its entirety. Clinicians then parse out key clinical findings that would lead to the appropriate diagnosis and treatment plan. This level of nuanced understanding and experience cannot be fully tested by this multiple-choice format. Moreover, the restricted pool of post-training data AAO cases made it difficult to conduct a granular analysis of the GPT models’ performance across different types of questions. This limitation is particularly relevant when considering the possibility that these models might excel or falter based on specific question categories.GPT-4.0 outperformed its predecessor GPT-3.5 in answering the AAO “Diagnose This” set of multiple-choice ophthalmic clinical cases, especially in neuro-ophthalmology, with improved accuracy even in image-related questions. Within the span of a couple of months, these vastly improved GPT-4.0 results not only signify the rapid pace of technological advancement in machine learning but also underscore the transformative potential AI holds for affecting ophthalmic student education and medical practice. AI models could serve as a supplementary educational tool for ophthalmology students, providing instantaneous, data-driven feedback that could enrich traditional learning environments. However, it is not without limitations, including the risk of generating fictional information and weakness in more complex inference-based cases. Future studies should not only focus on accuracy but also assess the level of concordance between AI response rationale and expert consensus. This could help in refining the algorithms and ensuring they align more closely with established medical knowledge and clinical best practices.As we contemplate the increasing role of AI in medicine, it is crucial to remember the ethical and professional obligations that physicians carry. Technology should be seen as a complement to, not a replacement for, the expert. Doctors are ultimately responsible for patient outcomes, and thus, they need to be cautious and selective in the kinds of technology they incorporate into their practice and training regime.


We integrated graphene with asymmetric metal metasurfaces and optimised the geometry dependent photoresponse towards optoelectronic molecular sensor devices. Through careful tuning and characterisation, combining finite-difference time-domain simulations, electron-beam lithography-based nanofabrication, and micro-Fourier transform infrared spectroscopy, we achieved precise control over the mid-infrared peak response wavelengths, transmittance, and reflectance. Our methods enabled simple, reproducible and targeted mid-infrared molecular sensing over a wide range of geometrical parameters. With ultimate minimization potential down to atomic thicknesses and a diverse range of complimentary nanomaterial combinations, we anticipate a high impact potential of these technologies for environmental monitoring, threat detection, and point of care diagnostics. Within the same MIR spectral range, black body radiation can also be utilised for photodetection and thermal imaging technologies. Taken together, MIR technologies have a substantial role in environmental monitoring, medical diagnosis, and security. However, in comparison to other wavelength regions, there exists a relative lack of MIR sources, detectors, and methodologies.Distinctive mid-infrared (MIR) molecular vibrations, ranging between ~2 and 12 μm, act as characteristic ‘molecular fingerprints’ for label-free identification of a wide range of chemicals and biomolecules. Usefully, atmospheric transparency windows, at 3–5 μm and 8–13 μm, enable a range of applications such as COThe current state-of-the-art technologies for MIR sensors are based on semiconductor bulk or quantum structures, such as gallium arsenide (GaAs), indium antimonide (InSb), and mercury-cadmium-telluride (MCT) . Whilst A new family of low dimensional nanomaterials  offer unique optoelectronic functionalities and new technological solutions beyond those attainable with conventional semiconductors ,6,7,8,9.2, SiC, quartz) or from environmental and atmospheric dopants [One practical limitation is the effect of charge inhomogeneity and scattering arising from the underlying substrate  spectroscopy to understand the key geometric tuning parameters for optimising MIR interaction and wavelength selectivity. The role of graphene for optoelectronic hybrid metasurface devices was confirmed and clarified by finite-difference time-domain (FDTD) studies. Moreover, the technological approaches described are readily scalable for industrial applications, where there is a high demand in a wide range of applications such as air quality inspection, automotive vehicle safety systems, alcohol sensing, healthcare, and security.In this work, we demonstrated the design, fabrication, and characterization of hybrid-graphene metasurface devices towards geometrically tuneable molecular sensors for CO2 surface, thickness 192 nm, was thermally grown by standard PECVD on a commercially available double-side-polished silicon wafer, of thickness 525 μm . The wafer was diced into chips of dimensions 10 × 10 or 20 × 20 mm2. The nanostructured arrays were then defined with a Raith eLine Plus electron beam lithography system  using polymethyl methacrylate  spin-coated at 4000 r.p.m. for 1 min, baked at 180 °C. Initial geometrical design parameters were determined with input from FDTD studies. The metal nanostructures were deposited in an Edwards Auto 306 thermal evaporator  (vacuum pressure 10−6 mBar), to cover large surface areas (2 × 2 to 10 × 10 mm2) of the SiO2 substrates. Following metal lift-off and solvent based cleaning , the surface geometries were analysed by scanning electron microscopy (SEM), using the same eLine system.Asymmetric metal nanoantenna metasurface arrays were fabricated by electron beam lithography and optically characterised by micro-Fourier transform infrared spectroscopy. A SiODimensions of the metal surface arrays were determined with an approximate measurement uncertainty of 0.025 μm, originating from geometric non-uniformity of fabricated metal nanoantenna and SEM measurement precision. Both the array-to-array and wafer-to-wafer repeatability was found to be within this same uncertainty range. Deposited metal thicknesses were verified with a Dektak 150 Surface Profiler  within 2 nm. Thicknesses of the deposited Au nanoantenna structures were 50 ± 5 nm.2 substrates by an adhesive transfer method [Two approaches were implemented for integrating the metal metastructures with monolayer CVD graphene : (1) graphene transfer onto pre-fabricated and characterised metal arrays and (2) direct metal deposition on the graphene surface. Transferring graphene allowed direct comparison of optically characterized surfaces and reduced risks related to graphene–metal adhesion. Nominally single layer graphene was acquired from and transferred on to the fabricated and optically characterised metal arrays on SiOr method . The prer method , electri2, with resistivities of a few Ω on the Au contacts, indicating correctly isolated Au nanostructures.Graphene optoelectronic device structures were patterned by electron beam lithography, graphene plasma ashing, metallisation, and lift-off as described in . Materia2·V−1·s−1, with two dimensional charge carrier densities of ~1 × 1012 cm−2. At this measured charge carrier density, significant changes or asymmetry were not observed by comparing the resistivities of the asymmetric graphene–metal metastructures, suggesting that graphene conductivity dominates the device conductivity in the measured range.With the addition of graphene, two-dimensional sheet resistivities of the devices were estimated as ~0.7 ± 0.3 kΩ/square. By considering commercial material standardisation and comparing to previous characterisation data, the graphene device mobilities were estimated as ~1000 cm−1 (2–20 μm), with a resolution of 2 cm−1, measured over areas from 40 × 40 μm2 to 1 × 1 cm2. Polarization measurements used an additional model P03 IR Polarizer .Micro reflection and transmission Fourier-transform infrared spectroscopy (FTIR) measurements were performed by a VERTEX 80v FT-IR spectrometer attached to a HYPERION 2000 FTIR microscope , which allowed to measure both transmission and reflection spectra of the samples. The FTIR spectra were recorded in the range of 5000–500 cmd = 0.335 nm) of the graphene sheet. Dielectric coefficients of gold were numerically described using Lorentz equation with two poles by fitting refractive index and extinction coefficient data obtained from [x and y directions, while the ends of the calculation domain in the z dimension were simulated by perfectly matched layers (PMLs). Time step duration in the FDTD calculations was 1.83 × 10−13 s, while a mesh size of 0.012 nm was used, so that detailed features of the electromagnetic wave within the single graphene sheet were properly resolved. The total number of simulated time steps was 16 × 105; it was sufficient that all fields propagated away from the calculation domain of the FDTD study. The FDTD code required up to 200 GB RAM in a mini-supercomputer Intel(R) Xeon(R) 144 cores, 500 GB RAM, 20 TB HDD.The applied FDTD code was developed in-house, as described in several previous studies ,47,48. Tned from . PeriodiMetasurfaces, comprised of asymmetric metal nanoantenna arrays, were initially designed based on FDTD studies, which indicated strong interactions with electromagnetic waves, even for sparse metal arrays, with significantly enhanced reflectance (85%), a substantial diffraction (10%), and a much-reduced transmittance (5%) for an array of only 15% surface metal coverage . Importa2-Si substrates, followed by description and analysis of the integrated hybrid graphene metasurfaces. zL = 50 nm ≈ MIRλ/80), width (yL = 200 nm), and nanogap lengths (xg = xP − xL = 200 nm) remained fixed, whilst the metal length (Lx) and lateral pitch (Py) were varied. Metal coverages were estimated as M% = (xL × yL)/(xP × yP).Since the direct interaction of unpatterned graphene in the midinfrared can be relatively weak, in this approach, we began with fabrication and analysis of metal nanoantenna metasurface on SiOPy. Two main transmittance minima were observed, defined here as λ1 and λ2. The second transmittance minima, around 9–10 μm (λ2), was also observed for unpatterned reference regions of Si–SiO2 (SiO2 layer thickness 192 nm), which was attributed to asymmetric Si–O stretching modes for the sample substrate [λ1, displayed around 4 μm, was absent for the unpatterned Si–SiO2 regions. This minimum depended strongly on the IR polarization and metal geometry, and can be fully attributed to the patterned metasurfaces. Peak interaction strengths  were observed to increase with the metal coverage in the range of 3% (Py = 10 μm) − 20% (Py = 1.2 μm). For higher density arrays of nanoantenna, the intensity increases appeared to saturate, with a slight broadening of the peak.The MIR photoresponse of the fabricated metal metasurfaces was investigated by micro-FTIR spectroscopy. ubstrate . However2 substrates. Both λ1 and λ2 displayed geometry dependant tuneability. It was found that the primary geometric variable was the nanoantenna metal length xL, whilst an important perturbation was induced by the lateral pitch yP. The shorter infrared peak wavelength response λ1 exhibited the strongest dependence on the array geometries. As with λ1, transmittance minimum λ2 was also observed to shift with varying metal length and lateral pitch . However, the shift in λ2 was significantly less sensitive to changes in the geometry, only ranging between ~9.2 and 10.0 μm for the fabricated structures. The smaller observed blueshift in λ2, at high metal coverage, was understood to be the effect of the non-symmetric density-of-states of the Si–O vibration modes in the three-dimensional SiO2 layer (thickness 192 nm) [λ2 was the result of the transiently concentrated electromagnetic field around the surface nanolayer which strongly activates the Si–O vibration modes in the SiO2 layer. Midinfrared photoresponses were qualitatively similar for both peaks from room temperature down to 10 Kelvin, suggesting temperature resilient performance, however with an apparent blueshift of approximately 150 nm at low temperatures.The peak response wavelengths of the main MIR features were investigated as a function of the metasurface geometry.  192 nm) ,52. The 2 dielectric thickness. Combining this information, enabled reliable reproduction of metasurfaces with optimised and well-defined photoresponse for integration with graphene, and towards devices for targeted molecular sensing applications.The characterised photoresponse for both features were also observed to be less critically sensitive to other geometric factors, varied within ~50%, such as metal thickness, metal width, longitudinal pitch, nanogaps, and SiO2–metal metasurfaces, enabling direct comparison of the photoresponse [2, with the persistent presence of λ1 and λ2  and (2), τ = 100 fs are adopted from [2 V−1 s−1, whilst the experimental graphene properties in this study were estimated as 1000 cm−2/V s (at ~1 × 1012 cm−2). By scaling τ proportional to mobility, we obtain a new τ = 100/2.7 fs. Such a modification does not affect Equations (1) and (2), since 1/τ = 1.0/100.0 × 10−15 = 1.0 × 1013 s−1, 1/(100 × 10−15/2.7) = 2.7 × 1013 s−1, while in the wavelength range of interest, ω = 2π c/λ = 6.28 × 3 × 108/4.0 × 10−6 = 4.7 × 1014 s−1 is much larger than the τ−1 factor.To understand our experimental results, we theoretically investigated infrared pulse transmission through hybrid graphene metasurfaces by the FDTD method. The wavelength range of interest was within the midinfrared range, 2–10 μm, so the intraband conductivity of graphene sheet is adopted ,53:(1)σ=ted from ,53. In [2-Si. This sounds reasonable, since the wavelengths of the IR waves were much larger than the sub-nano-feature sizes of the graphene sheet (0.335 nm); so, macroscopically, the IR waves should transmit through without perceivable perturbation. However, previous studies reported significant crests and troughs in the IR transmission spectra through single graphene sheet, e.g., [The first theoretical study subject was to understand the almost identical FTIR spectra of graphene and removed graphene in t, e.g., .xy plane positioned at z = 0 is easily calculated by applying Fresnel’s equationsrE, iE, and tE denote the electric field of the reflected, incident, and transmitted wave, respectively, between medium 1 denoted by complex refractive index ñ1 and medium 2 having ñ2. Let ñ = n + iκ be the complex refractive index of the thin graphene film (denoted as medium 2), and the measurement is performed in air so that the refractive indices of the spaces above the upper interface of the graphene sheet (medium 1) and below the lower interface of the graphene sheet (medium 3) are 1.0. By Equation (3), the reflection and refraction coefficients at the upper and lower interfaces arer12 is the reflection of the plane wave from medium 1 back to medium 1 reflected by the upper interface of the graphene sheet, t12 is the refraction from medium 1 into medium 2 at the upper interface. r23 and t23 are likewise defined but at the lower interface. Note that r12 = −r23. The series of the transmitted waves areδ = ωñd/c0 and β = iδer23r21. It is easy to see that the result of the above infinite summation isWe started with a single graphene sheet in vacuum. The transmittance of the IR plane wave at normal incidence through this single graphene sheet extended in the iS = 2c0ε0iE2 from which we obtain the transmittance T through the thin graphene filmSince the optical power of the transmitted light isn, then Equation (6) for n = ∞ (the black line marked with “∞”) are presented in n = ∞, the transmittance is very close to 1.0 due to the extremely thin layer thickness of the graphene sheet (d = 0.335 nm) and appears featureless as a function of the IR wavelength. However, when n is limited, strong oscillation in the transmittance spectrum is observed.The numerically calculated transmittance, by Equation (5) with limited summation over Next, we performed FDTD numerical calculations using in-house FDTD codes ,47,48. Nxy plane, and the IR pulse impinged on the graphene structure along the z axis . When we calculated the transmittance as a function of n, as a means to emulate measurements of finite time intervals, Equations (5) and (6) produced crests and troughs in the transmission spectrum  but mainly the transmission.The critical aspect about results of Equation (6) and FDTD calculation is that Equation (6) is derived for a time interval of infinite length, i.e., trum see a, similaEx field of the transmitting electromagnetic field is shown in 5 (the time duration of each time step is δt = 1.83 × 10−13 s in FDTD); we still observed significant EM field passing through the transmission detector, which caused the strong oscillation in the FDTD-calculated transmission spectrum in This also explains the time-dependence of the FDTD-calculated transmission spectrum presented in 2 then covered with a single graphene sheet schematically shown in 2/Si substrate. The size of the metal patch was denoted as xL × yL × zL, where xL = 1.4 μm is the metal length in the x direction, yL = 0.2 μm is the metal width in the y direction, and zL = 0.05 μm is the metal thickness in the z direction. The periods of the array are denoted as xP = 1.6 μm and yP = 1.2 μm in the x and y direction, respectively.Here, we studied periodic gold nanoantenna metasurfaces embedded in SiOz propagation direction to propagate along the x and y directions, which persist a long time since the sub-micron structures are periodic along the xy plane. Only after ~12 × 105 simulation steps, all transmitted EM field passed through the detector and the transmittance spectra converge  are not displayed in the simulated spectra.Similar to rvations . Note th2 substrates. A weaker blueshift, but much enhanced reflectance was also observed for the second photoresponse peak λ2 due to the Si–O vibration mode in the SiO2 layer , were observed both from (i) direct comparison by graphene transfer onto pre-characterised metal metasurfaces and (ii) indirect comparison by patterning identical metal metasurfaces on graphene-covered- or bare- SiOO2 layer .1 for these devices.For the application relevant PMMA encapsulated (200 nm) metal metasurfaces and hybrid-graphene devices , FTIR reThese effects can have a significant practical impact on the design and functionality of such devices, where the optical response may require precise tuning for suitable operational efficiency. For example, for the application of molecular sensing where the absorption wavelengths can be relatively narrow. The demonstrated influence of the graphene layer, with indications of the range of sensitivity and application, also suggests the possibility of a further dynamic tunability of the system by modifying the graphene properties by electrostatic or electrochemical gating . For a mLx, the lateral pitch Py, and the material properties at the metasurface. From [Nm is an integer denoting the order of the resonance condition, effn is the effective mode refractive index, and Lδ represents the deviation of the effective metal length from the geometrical length xL. Nλ/xL) vs. (1/yP), revealing the overall trend for the metasurface arrays to be approximated byA(SiO2-Au) ≈ 4.12, b(SiO2-Au) ≈ 1.07 for metasurfaces on SiO2 substrates. With the addition of graphene, we find A(SiO2-Au-G) ≈ 3.72, b(SiO2-Au-G) ≈ 1.32. From this, we can estimate neff(SiO2-Au) ≈ 2.06, and neff(SiO2-Au-G) ≈ 1.86. This pitch related shift effect can be quite substantial for subwavelength periodicities, e.g., for yP = 0.6 μm, ∆λ1 ≈ −40%.The main factors determining the peak photoresponse wavelengths and interaction strength were experimentally observed to be the metal length ce. From , we can 2, The extraction of accurate fitting relations can further be used to improve the design precision of the metasurface geometries. This simple photoresponse design method was used to define the geometric parameters within this study to target specific wavelengths, for example in The physics of the blueshift due to the insertion of the graphene sheet into the metal metasurface can be interpreted from two perspectives: (1) The electromagnetic wave is strongly perturbed, transiently, by the graphene sheet. This, in turn, affects the free electrons in the metal nanoantenna, resulting in a stronger plasmonic effect and a blueshift; (2) by viewing the graphene and the metal nanoantenna independently, the nanoantenna encloses a space in the form of a resonant cavity. The insertion of the graphene sheet reduces this cavity volume so that the resonant frequency is increased, resulting in a blueshift. Since this affect is expected to be influenced by the graphene charge carrier density, it is anticipated that the spectral photoresponse wavelength of the hybrid graphene metasurfaces can be further tuned or modulated by electrostatic or electrochemical gating ,39,40.In summary, we demonstrated simple, precise, and reproducible geometrical photoresponse tuning of hybrid graphene metasurface towards targeted molecular sensing. Systematic midinfrared photoresponse characterisation was enabled by a combination of electron beam lithography-based nanofabrication, micro-FTIR spectroscopy, and FDTD studies. Peak-response wavelengths were found to depend most critically on two geometrical parameters; the longitudinal metal nanoantenna length and the lateral pitch between the antenna. Substantial blueshifts were observed and characterised upon the integration of graphene with the metal metasurfaces, and for high density metasurface structures, observed up to ~40%. The careful interpretation of more than 100 unique structures enabled the development of a simple and precise set of design tools to ensure geometrically fine-tuned photoresponses for reproducible mid-infrared molecular targeting. The combination of hybrid metasurfaces and their detailed characterisation is important for the next generation of smart portable sensors and lab-on-chip technologies.


Based on the extraordinary electromagnetic properties of terahertz waves, such as broadband, low energy, high permeability, and biometric fingerprint spectra, terahertz sensors show great application prospects in the biochemical field. However, the sensitivity of terahertz sensing technology is increasingly required by modern sensing demands. With the development of terahertz technology and functional materials, graphene-based terahertz metasurface sensors with the advantages of high sensitivity, fingerprint identification, nondestructive and anti-interference are gradually gaining attention. In addition to providing ideas for terahertz biosensors, these devices have attracted in-depth research and development by scientists. An overview of graphene-based terahertz metasurfaces and their applications in the detection of biochemical molecules is presented. This includes sensor mechanism research, graphene metasurface index evaluation, protein and nucleic acid sensors, and other chemical molecule sensing. A comparative analysis of graphene, nanomaterials, silicon, and metals to develop material-integrated metasurfaces. Furthermore, a brief summary of the main performance results of this class of devices is presented, along with suggestions for improvements to the existing shortcoming. Terahertz supersurfaces and graphene materials for spectroscopic analysis.Elucidating the mechanism of terahertz detection of biochemical molecules.Application of terahertz to biochemical molecule detection. Terahertz waves are electromagnetic waves with a wavelength of 0.03–3 mm and a frequency in the range of 0.1–10 THz . CurrentTerahertz waves lie between infrared rays and microwaves. The response of biochemical molecules to terahertz radiation mainly comes from collective vibrational modes, such as vibrations and conformational bending, determined by biochemical molecular conformation and chemical bonding. Biochemical molecules dominate intramolecular vibrations, including weak interactions, hydrogen bonds, van der Waals forces, etc. –6, proviThere is a need to find high-performance and stabilizing terahertz devices. Graphene as a two-dimensional material has been widely used in recent years . CombiniThe challenges faced by terahertz biomolecular sensors include metasurfaces that have the ability to be efficient and selective in capturing specific biomolecular target substances. To enhance the detection of biochemical molecules target substances on the metasurfaces improve the detection effect . Among tIn the detection of terahertz metasurfaces sensing, when terahertz waves pass through the metasurfaces unit structure and some periodic subwavelength resonators with small thickness are added to the medium, the distributions of the reflection and refraction field at the boundary of the medium determine the boundary of the homogeneous medium, two reflect and refraction properties . TeraherThe four of valence electrons populate the sp orbitals, forming an extra-domain electron cloud, making graphene with ultra-high carrier migration, and resonance conditions of the electric field in order to excite the plasma resonance, graphite can be actively modulated by doping and applying gate pressure , 58, as Surface plasmon resonance (SPR) is also a sensing mechanism, and sensors based on SPR through the interaction between the sensing element and the metasurfaces , 61. ThiConsidering the scattering of the graphene plasma on the time discontinuity for a confined plasma, the projection and reflection coefficients of the photons in the plasma are analyzed, as given in Eqs. –6). It . It 6). Terahertz biochemical sensing developed by applying graphene involved in the design of the metasurfaces has enabled the detection of biochemical molecules such as; proteins, nucleic acids, viruses, pesticides, and other biochemical molecules. The electromagnetic environment in the vicinity of the graphene metasurfaces sensing can be significantly enhanced by the change in Fermi energy level caused by external induction, graphene metasurfaces to enhance sensor detection sensitivity.Terahertz waves are associated with subwavelength resonant cell unit structures and material properties. Terahertz wave metasurfaces are combined with graphene, and their metasurfaces interact to trigger plasmon resonant modes, circulating dipole polarization, Fano resonances, and capacitive resonance , 68. TheQ), sensitivity (S), and resonance characteristics (FOM) [Q reflects the quality of the resonance and reflects the resonance field enhancement produced by the metasurfaces resonance unit. The Q value is defined as  –72. The fined as .9\documef is the resonant center frequency and FWHM is the half-peak width.When the biochemical molecules to be detected cause changes in the frequency of the resonance spectrum, it can be used for the purpose of qualitative and quantitative detection of the biochemical molecules to be tested measured, and real-time detection of targets. Sensitivity is an index to measure the change of resonant frequency of biochemical target molecules with the refractive index, and it is an important index for the detection performance of terahertz metasurface detection, which can be expressed as .10\documf is the resonance shift and Δn is the amount of variation in the molecular change properties of the target to be measured. Resonance characteristics FOM to compare the sensing performance of metamaterial sensors. FOM can be expressed as Eq. (Δd as Eq. .11\documThe FOM reveals the resonant properties of the sensor metasurface itself and the corresponding capabilities of the measured biochemical molecules. This equation can be understood as the relationship between the factors controlling the detection performance to balance the resonance characteristics of the sensor device and improve the interaction with the biochemical molecules to be detected. Research into terahertz sensor technology and detection is a top priority.Graphene has been identified as one of the most attractive materials in recent decades . GraphenGraphene terahertz biosensor is a new detection and analysis method based on the development of the terahertz wave spectroscopy analysis method, as shown in Fig. −1 is achieved. Meanwhile, Zhang et al. [Ruan et al. developed an ultrasensitive terahertz biosensor with a graphene, waveguide hybrid Fano structure resonance as shown in Fig. g et al.  proposed2 as a substrate, with a single layer of graphene film inserted between the vertical structures as shown in Fig. −1. Graphene-based biosensors or gas sensors have promising applications in the terahertz frequency range.Tang et al.  proposedQ-factor of 13.76, the resonance peak of refractive index sensitivity of the analyte to be measured, and sensitivity of 851 GHz/RIU results, as shown in Fig. Nickpay et al.  proposedGraphene sensor devices with good selectivity, sensitivity and other advantages have attracted much attention , 86. GraProteins are the executing molecules that respond to instructions stored in the genetic material of any life form, and protein-targeted sensors can be made by immobilizing specific biomolecules on a surface . However16-22 peptides are the main marker of Alzheimer's disease. Xu et al. [16–22 peptides as shown in Fig. 16–22 peptides due to the change of the Fermi energy level leading to the change of the position of the Rabi splitting peak as in Fig. 16-22 decreases, the Fermi energy level of p-doped graphene moves away from the Dirac point with the aggregation process, and the movement of the Fermi energy level of graphene is important due to the stacking of the π–π bond between Aβ16-22 peptides and graphene.The Aβu et al.  proposedYao et al.  developeParmar et al.  proposedMetasurfaces learned by this method allow the detection of other components such as metal ions, carbon dioxide, other aromatic molecules, etc. . TherefoDNA is a macromolecular polymer composed of deoxyribonucleic acid. It is a biomolecule of biological genetic information. It is very critical for the detection of biological macromolecules to provide information on living individuals, and DNA biosensors are widely used in different fields of clinical medical diagnosis, environmental detection, and drug development , 92. Theπ–π stacking with DNA molecules tightly bound; the nano-slot resonance assisted by strongly enhanced and aggregated terahertz waves, even in the presence of nmol/mm2 levels, can be observed [By designing graphene-combined nano-grooves with terahertz metasurfaces as shown in Fig. observed . More DNXu et al.  the applYang et al.  the propE. coli O157:H7 first. Selective detection of 100 nM DNS solution was achieved. Through mechanistic analysis due to n-type molecules doped by non-covalent surface, including van der Waals forces, π–π stacking, and hydrogen bonding. The p-type doped graphene Fermi energy levels move to the Dirac point, in both structures graphene on the metasurfaces of a large number of molecules will move the Fermi energy level to the Dirac point, resulting in the formation of the original graphene has a lower photoconductivity, thus higher transmittance and microfluidics binding to provide highly sensitive detection [Meanwhile, metasurfaces-assisted terahertz real-time label-free biosensors have attracted extensive research, and high sensitivity in the terahertz range is still challenging for specific detection of highly absorbing liquid samples or water molecular liquids, water has strong absorption of terahertz waves, to overcome these drawbacks researchers have used a microfluidic technique combined. Zhou et al.  developeetection .Fig. 17TAs an efficient platform for biosensing in liquid environments, it can selectively and label-free detect biomolecules. By changing the corresponding DNA sequence, the sensor can be altered to detect other different DNA molecules. It results in that the graphene-bound terahertz metasurface platform not only affords a broad research field for future medical applications but also proposed theoretical support for the optoelectronic effects of thin films such as 2D materials.Terahertz propagating in free space interacts weakly with the sample and is not sufficient for trace detection. By transferring graphene to the sample surface, the metasurfaces absorber constructs a graphene metasurfaces absorber heterogeneous structure as shown in Fig. The accurate and rapid detection in the terahertz band of this study contribute a novel mean of detection. Zhao et al.  proposedDing et al.  developeA bifunctional covalent scaffold for modification with metasurface functionalization was reported by Esteban Piccinini et al.  as shownThe graphene nanomaterials transformed metasurfaces devices have gained the attention of researchers. Graphene metasurfaces heterostructures become a research thermoelectric for terahertz wave modulators , 104. GrAlthough there are many reports on graphene terahertz ultra-surfaces, there are still many difficulties to develop from the research field to the commercial field, including the detection limit of graphene terahertz ultra-surfaces in practical analysis, the stability of biochemical molecules, and the hysteresis of biomolecule detection . Its nonIn summary, the combination of metasurfaces and optical matter obviously enhances the effect of becoming the focus of research on terahertz sensing technology. The development of devices using graphene metasurfaces has made substantial progress. The research using graphene metasurfaces as a starting point using different substrates and materials combinations has significantly enriched the relevant research results. The application of graphene terahertz sensor devices faces many challenges, and we need precise control of graphene generation, homogeneity, and surface functionalization. Current research on terahertz sensing detection technology requires two enhancements. The first of which is the design of the super-surface unit structure, and the reasonable selection of the substrate material is crucial for enhancing terahertz sensing to obtain stronger resonance properties. The second is the effective binding of the molecule to be measured to the target molecule by combining functionalized structural materials and optimization of analytical conditions. This is done while reducing terahertz loss to enhance the sensitivity of detection. However, it is promising that many graphene terahertz devices have achieved fast response and selectivity. Sensing technology is the interconnection of multidisciplinary technologies. With the development of novel spectroscopic techniques, data analysis algorithms, and materials science fields, technological advances are expected to promote the flourishing of biochemical sensing in biochemical detection, device performance, and applications in the foreseeable future.


ChatGPT is a large language model that has performed well on professional examinations in the fields of medicine, law, and business. However, it is unclear how ChatGPT would perform on an examination assessing professionalism and situational judgement for doctors.We evaluated the performance of ChatGPT on the Situational Judgement Test (SJT): a national examination taken by all final-year medical students in the United Kingdom. This examination is designed to assess attributes such as communication, teamwork, patient safety, prioritization skills, professionalism, and ethics.All questions from the UK Foundation Programme Office’s (UKFPO’s) 2023 SJT practice examination were inputted into ChatGPT. For each question, ChatGPT’s answers and rationales were recorded and assessed on the basis of the official UK Foundation Programme Office scoring template. Questions were categorized into domains of Good Medical Practice on the basis of the domains referenced in the rationales provided in the scoring sheet. Questions without clear domain links were screened by reviewers and assigned one or multiple domains. ChatGPT's overall performance, as well as its performance across the domains of Good Medical Practice, was evaluated.Overall, ChatGPT performed well, scoring 76% on the SJT but scoring full marks on only a few questions (9%), which may reflect possible flaws in ChatGPT’s situational judgement or inconsistencies in the reasoning across questions (or both) in the examination itself. ChatGPT demonstrated consistent performance across the 4 outlined domains in Good Medical Practice for doctors.Further research is needed to understand the potential applications of large language models, such as ChatGPT, in medical education for standardizing questions and providing consistent rationales for examinations assessing professionalism and ethics. ChatGPT is a large language model developed by OpenAI, which uses deep learning to provide responses to natural language input, by identifying the relationships between words and by generating coherent responses . It achiThese recent advances in language models demonstrate the potentially significant impact of artificial intelligence (AI) technologies on digital health. ChatGPT has already demonstrated its ability to pass professional examinations for postgraduates in the fields of law  and busiWith regard to the United States Medical Licensing Examination (USMLE), ChatGPT scored at, or near, the pass mark for each step of the examination . AlthougThe Situational Judgement Test (SJT) aims to assess many of the skills not covered in the USMLE  and in oOur aim was to evaluate the performance of ChatGPT on the SJT and determine how well it performs across the 4 key domains of Good Medical Practice. To our knowledge, this is the first study to investigate the performance of ChatGPT on a situational judgement and professionalism examination of this type.The ChatGPT model was trained on vast amounts of data from the internet, up to and including 2021, after which it has not been connected to the internet . Hence, The SJT examination is divided into 3 sections, with each question stem first introducing a scenario, followed by a question on how the candidate would approach the situation. These sections include (1) rating the appropriateness or importance of a response, action, or consideration; for example, very appropriate, appropriate, somewhat inappropriate, or inappropriate; (2) multiple-choice questions asking for the 3 most appropriate options from among 8 options; and (3) ranking the appropriateness, or importance, of 5 different actions or considerations in response to a scenario. The SJT is scored on a scale from 0 to 50 points and is not a pass-or-fail examination.Given the discrepancies in correct answers, and justifications among unofficial study resources, we used the most recent official 2023 SJT practice paper, which is publicly available from the official United Kingdom Foundation Programme Office (UKFPO) website , togetheEach question from the 2023 SJT practice paper was formatted identically into the ChatGPT text with the following additions: (1) the official candidate examination instructions were provided before each scenario , and (2)We used the official UKFPO scoring templates to determine the number of marks scored by ChatGPT in each of the 3 sections of the examination. The scoring for each question is not binary, and partial marks are awarded for answers that are nearly correct. For example, in the multiple-choice section, each question has 3 correct answers from a choice of 8 options; each correct answer is awarded 4 points with a maximum of 12 points per question. Therefore, a candidate can score 0, 4, 8, or 12 marks for each multiple-choice question. The rating and ranking sections award partial marks for an answer that is close to the correct one. ChatGPT’s performance was calculated as a percentage for each section using the official UKFPO scoring templates. We also determined the proportion of questions for which the answers were correct (defined as scoring 100% of the available marks for the given question), mostly correct (50%-99%), and mostly incorrect (<50%) for each section.The final SJT score provided to candidates is on a scale from 0 to 50, which is based on test-equating and scaling the raw marks achieved on the paper. This conversion formula varies between sittings and is not made publicly available by the UKFPO. We, therefore, reported ChatGPT’s performance as a percentage instead of reporting it on the 0-50–point scale, which is normally used to compare performance between human candidates. Both the SJT and Educational Performance Measure scores determine a final-year medical student’s ranking when applying to the Foundation Programme. The Educational Performance Measure is a measure of performance in medical school up to the point of application to the Foundation Programme with students grouped into deciles.In order to assess ChatGPT’s performance across the different domains of Good Medical Practice, each question was categorized into at least 1 domain. To classify the questions, we used the 2023 practice paper answer sheet provided by the UKFPO, which also contains the rationale for most answers. Many of the rationales contained direct references to at least 1 domain from the Good Medical Practice guidelines that were used for categorization. Questions with rationales, which had missing links to the domains, were categorized by 2 independent reviewers on the basis of both the question itself and the rationale provided by the UKFPO. Both reviewers recently completed the Foundation Programme on which this SJT examination is based. The reviewers were blinded to each other’s categorization of each question, and disagreements were resolved by a third reviewer who was a consultant radiologist within the National Health Service and was blinded to the categorizations made by the 2 initial reviewers. Once all questions in the examination were assigned domains combining the rationales offered by the UKFPO and the screening approach used for the remaining questions, ChatGPT’s performance in each domain of Good Medical Practice was assessed using the official scoring templates and reported as a percentage.A summary of the workflow for this study including sourcing, encoding, adjudicating results, and assessing performance can be found in This study did not involve human or animal participants and ethics approval was not required.Overall, ChatGPT scored 76% (929 of a possible 1217 marks) on this SJT examination .For the rating section of the examination, ChatGPT scored 78% (197/253 marks) with 0% (0/18 questions) entirely correct, 100% (18/18 questions) mostly correct, and 0% (0/18 questions) mostly incorrect responses .For the multiple-choice section of the examination, ChatGPT scored 65% (172/264 marks) with 23% (5/22 questions) entirely correct, 50% (11/22 questions) mostly correct, and 27% (6/22 questions) mostly incorrect responses .For the ranking section of the examination, ChatGPT scored 80% (560/700 marks) with 6% (2/35 questions) entirely correct, 94% (33/35 questions) mostly correct, and 0% (0/35 questions) mostly incorrect responses .There were 170 questions and answer statements that were classified into at least one of the GMC domains. Of these, 84  were explicitly linked to a GMC domain within the rationale provided in the UKFPO’s official answer sheet. The independent reviewers then screened the remaining 86 and agreed on which GMC domains they applied to for 76 (88%) of them. The remaining 10 were then assessed by the tiebreaker (consultant radiologist), and their labels were used for the analysis.ChatGPT scored 78% (328/419) in the Knowledge, Skills and Performance domain, 76% (484/635) in the Safety and Quality domain, 76% (340/448) in the Maintaining Trust domain, and 75% (784/1046) in the Communication, Partnership and Teamwork domain.In the rating section, ChatGPT’s worst performance was noted on a question related to the appropriateness of specific actions after discovering that a medical student has likely acquired detailed information about the scenarios that will feature in an upcoming examination. The official answers and rationale advise that it would be (1) somewhat appropriate to inform the medical student that their Educational Supervisor will be informed about the situation and (2) very appropriate to encourage the student to inform the medical school that they have acquired information about the examination. ChatGPT labeled these options as inappropriate and somewhat inappropriate, respectively, with the rationale that (1) “Threatening to inform her Educational Supervisor about her behaviour is not a productive or supportive approach…It is important to remember that as a facilitator, the doctor’s role is to support and guide the student in their learning, not to police their behaviour” and (2) “While it is important [for the student] to be honest about how she obtained information, encouraging her to declare this to the medical school may be premature at this point. It may be more appropriate to first have a conversation with [the student] to understand why she sought additional information and to provide guidance on appropriate conduct.”In the ranking section of the examination, ChatGPT scored its lowest marks (50%) on a question asking about the appropriateness of certain actions when one has arrived home after one’s shift and realizes that they forgot to handover an urgent blood sample that needs to be taken today. ChatGPT deemed returning to the ward immediately to perform the blood test as the most appropriate action, whereas the official marking labeled this is as one of the less appropriate options. ChatGPT also ranked telephoning the ward and leaving a message with the nursing team as a less appropriate option because “the nursing team may not have the necessary information or authority to take appropriate action for the patient,” while the official marking classified this as one of the more appropriate actions.We evaluated the performance of ChatGPT on the SJT: a national examination for final-year medical students in the United Kingdom, which assesses attributes including communication, teamwork, patient safety, prioritization skills, professionalism, and ethics. Overall, ChatGPT scored 76% on the examination. It answered 0%, 23%, and 6% of the questions entirely correctly in the rating, multiple-choice, and ranking sections of the examination, respectively, but was mostly correct for 100%, 50%, and 94% of the questions in these sections. ChatGPT scored consistently across the 4 key domains of Good Medical Practice.ChatGPT’s overall performance was impressive considering that it was correct or mostly correct for the majority of questions in the examination. However, the proportion of the questions that were answered with 100% accuracy was lower than expected with its best performance being in the multiple-choice section, in which it chose the 3 correct options in approximately one-fourth of the questions. This could be due to flaws in ChatGPT’s reasoning in some of these situations. However, ChatGPT’s low proportion of entirely correct (100%) answers may also reflect inconsistencies within the examination itself. Several reports and student commentaries have suggested that there are significant discrepancies in the correct answers chosen by different experts . If thisInterestingly, ChatGPT scored 65% in the multiple-choice section versus 78% and 80% in the rating and ranking sections, respectively. This may reflect that this large language model is better suited to tasks that involve ranking and prioritization rather than selecting from a list of most appropriate, or relevant, options for a given scenario. ChatGPT has been trained on a wide gamut of data available from the internet, which may not always be factually correct, but amalgamated together means that the model may be more competent at dealing with open-ended questions which involve listing options in order of importance or relevance, as opposed to questions with individual correct answers.ChatGPT performed consistently across the 4 domains of Good Medical Practice, having scored between 75% and 78% across them. ChatGPT performed slightly better in the Knowledge, Skills and Performance and Safety and Quality domains than in the Communication, Partnership and Teamwork domain. We speculate that this could be explained by questions pertaining to knowledge and safety being more objective in nature, whereby patient safety and delivering high-quality care are always prioritized. These types of scenarios may provide ChatGPT with a more straightforward approach to classifying the appropriateness of the options, compared to questions pertaining to communication and teamwork where decision-making is more subjective and nuanced. However, the differences in ChatGPT’s performance across these domains were too small to provide more definitive insight.There were several limitations in this study: First, in practice, the raw score for this examination is converted to a 0-50–point scale, which is based on test-equating and a scaling conversion method that is not publicly available. We also do not have access to the results of medical students taking this practice examination and are therefore unable to directly compare ChatGPT’s performance to that of final-year medical students. Second, the answer sheet and rationales provided by the UKFPO for this examination only explicitly linked 49% of the questions and answer statements to the GMC domains outlined in Good Medical Practice. We therefore devised a method to link the remaining questions to the domains, which involved 2 independent reviewers and a tiebreaker, the results of which may have differed from those of the UKFPO. Third, many questions pertained to more than 1 domain of Good Medical Practice; hence, there was an overlap in questions across different domains when assessing ChatGPT’s performance in each domain. Fourth, our search was run on the February 2023 version of ChatGPT, and given the constant development of this large language model, future iterations may yield different outcomes.Overall, ChatGPT performed well in the examination but scored 100% for only a few questions, which may reflect inconsistencies in the examination or errors in ChatGPT’s reasoning (or both). This builds on the existing literature by demonstrating that AI-driven large language models such as ChatGPT not only perform well on a wide range of clinically based examinations, but also offer, for the most part, rational responses to professional and ethical dilemmas faced by doctors. Future research should focus on identifying patterns and inconsistencies in the ethical approaches of AI language models and mitigating potential biases in them. Directly comparing the performance of these types of models with that of human candidates in relation to situational judgement dilemmas will provide more direct insight into their performance relative to that of humans. If the ethical foundations of models such as ChatGPT are deemed appropriate and reliable, it would provide the opportunity for integration directly into medical education with, for example, interactive platforms, simulated scenarios related to situational judgement, and personalized feedback, as well as standardization of examinations. Finally, in order to achieve this, it will be crucial to use a collaborative approach among experts in AI, medicine, and medical education to realize the full potential of these new technologies. Addressing these points will help develop this field and promote the integration of large language models, such as ChatGPT, into medical education, thus helping to standardize assessments that evaluate professionalism and ethics while maintaining high-quality and equitable medical education standards.