Artificial intelligence in healthcare
Artificial intelligence (AI) in healthcare is the use of complex algorithms and software to emulate human cognition in the analysis of complicated medical data. Specifically, AI is the ability for computer algorithms to approximate conclusions without direct human input.

What distinguishes AI technology from traditional technologies in health care is the ability to gain information, process it and give a well-defined output to the end-user. AI does this through machine learning algorithms. These algorithms can recognize patterns in behavior and create its own logic. In order to reduce the margin of error, AI algorithms need to be tested repeatedly. AI algorithms behave differently from humans in two ways: (1) algorithms are literal: if you set a goal, the algorithm can't adjust itself and only understand what it has been told explicitly, (2) and algorithms are black boxes; algorithms can predict extremely precise, but not the cause or the why.

The primary aim of health-related AI applications is to analyze relationships between prevention or treatment techniques and patient outcomes. AI programs have been developed and applied to practices such as diagnosis processes, treatment protocol development, drug development, personalized medicine, and patient monitoring and care. Medical institutions such as The Mayo Clinic, Memorial Sloan Kettering Cancer Center, and the British National Health Service, have developed AI algorithms for their departments. Large technology companies such as IBM and Google, and startups such as Welltok and Ayasdi, have also developed AI algorithms for healthcare. Additionally, hospitals are looking to AI solutions to support operational initiatives that increase cost saving, improve patient satisfaction, and satisfy their staffing and workforce needs.  Companies are developing predictive analytics solutions that help healthcare managers improve business operations through increasing utilization, decreasing patient boarding, reducing length of stay  and optimizing staffing levels.

History

Research in the 1960s and 1970s produced the first problem-solving program, or expert system, known as Dendral. While it was designed for applications in organic chemistry, it provided the basis for a subsequent system MYCIN, considered one of the most significant early uses of artificial intelligence in medicine. MYCIN and other systems such as INTERNIST-1 and CASNET did not achieve routine use by practitioners, however.

The 1980s and 1990s brought the proliferation of the microcomputer and new levels of network connectivity. During this time, there was a recognition by researchers and developers that AI systems in healthcare must be designed to accommodate the absence of perfect data and build on the expertise of physicians. Approaches involving fuzzy set theory, Bayesian networks, and artificial neural networks, have been applied to intelligent computing systems in healthcare.

Medical and technological advancements occurring over this half-century period that have enabled the growth healthcare-related applications of AI include: 
 Improvements in computing power resulting in faster data collection and data processing
 Growth of genomic sequencing databases
 Widespread implementation of electronic health record systems
 Improvements in natural language processing and computer vision, enabling machines to replicate human perceptual processes
 Enhanced the precision of robot-assisted surgery

 Improvements in deeplearning techniques and data logs in rare diseases

Current research 
Various specialties in medicine have shown an increase in research regarding AI.

Radiology 
The ability to interpret imaging results with radiology may aid clinicians in detecting a minute change in an image that a clinician might accidentally miss. A study at Stanford created an algorithm that could detect pneumonia at that specific site, in those patients involved, with a better average F1 metric (a statistical metric based on accuracy and recall), than the radiologists involved in that trial. The radiology conference Radiological Society of North America has implemented presentations on AI in imaging during its annual meeting. The emergence of AI technology in radiology is perceived as a threat by some specialists, as the technology can achieve improvements in certain statistical metrics in isolated cases, as opposed to specialists.

Imaging 
Recent advances have suggested the use of AI to describe and evaluate the outcome of maxillo-facial surgery or the assessment of cleft palate therapy in regard to facial attractiveness or age appearance.

In 2018, a paper published in the journal Annals of Oncology mentioned that skin cancer could be detected more accurately by an artificial intelligence system (which used a deep learning convolutional neural network) than by dermatologists. On average, the human dermatologists accurately detected 86.6% of skin cancers from the images, compared to 95% for the CNN machine.

Disease Diagnosis 

There are many diseases and there also many ways that AI has been used to efficiently and accurately diagnose them. Some of the diseases that are the most notorious such as Diabetes, and Cardiovascular Disease (CVD) which are both in the top ten for causes of death worldwide have been the basis behind  a lot of the research/testing to help get an accurate diagnosis. Due to such a high mortality rate being associated with these diseases there have been efforts to integrate various methods in helping get accurate diagnosis’.

An article by Jiang, et al (2017) demonstrated that there are several types of AI techniques that have been used for a variety of different diseases. Some of these techniques discussed by Jiang, et al include: Support vector machines, neural networks, Decision trees, and many more. Each of these techniques is described as having a “training goal” so “classifications agree with the outcomes as much as possible…”.

To demonstrate some specifics for disease diagnosis/classification there are two different techniques used in the classification of these diseases include using “Artificial Neural Networks (ANN) and Bayesian Networks (BN)”. From a review of multiple different papers within the timeframe of 2008-2017 observed within them which of the two techniques were better.  The conclusion that was drawn was that “the early classification of these  diseases can be achieved developing machine learning models such as Artificial Neural Network and Bayesian Network.”  Another conclusion Alic, et al (2017) was able to draw was that between the two ANN and BN that ANN was better and could more accurately classify diabetes/CVD with a mean accuracy in “both cases (87.29 for diabetes and 89.38 for CVD).

Telehealth 
The increase of telemedicine, has shown the rise of possible AI applications. The ability to monitor patients using AI may allow for the communication of information to physicians if possible disease activity may have occurred. A wearable device may allow for constant monitoring of a patient and also allow for the ability to notice changes that may be less distinguishable by humans.

Electronic health records 
Electronic health records are crucial to the digitalization and information spread of the healthcare industry. However logging all of this data comes with its own problems like cognitive overload and burnout for users. EHR developers are now automating much of the process and even starting to use natural language processing (NLP) tools to improve this process. One study conducted by the Centerstone research institute found that predictive modeling of EHR data has achieved 70–72% accuracy in predicting individualized treatment response at baseline. Meaning using an AI tool that scans EHR data it can pretty accurately predict the course of disease in a person.

Drug Interactions 
Improvements in natural language processing led to the development of algorithms to identify drug-drug interactions in medical literature. Drug-drug interactions pose a threat to those taking multiple medications simultaneously, and the danger increases with the number of medications being taken. To address the difficulty of tracking all known or suspected drug-drug interactions, machine learning algorithms have been created to extract information on interacting drugs and their possible effects from medical literature. Efforts were consolidated in 2013 in the DDIExtraction Challenge, in which a team of researchers at Carlos III University assembled a corpus of literature on drug-drug interactions to form a standardized test for such algorithms. Competitors were tested on their ability to accurately determine, from the text, which drugs were shown to interact and what the characteristics of their interactions were. Researchers continue to use this corpus to standardize the measure of the effectiveness of their algorithms.

Other algorithms identify drug-drug interactions from patterns in user-generated content, especially electronic health records and/or adverse event reports. Organizations such as the FDA Adverse Event Reporting System (FAERS) and the World Health Organization’s VigiBase allow doctors to submit reports of possible negative reactions to medications. Deep learning algorithms have been developed to parse these reports and detect patterns that imply drug-drug interactions.

Creation of New Drugs 
DSP-1181, a molecule of the drug for OCD (obsessive-compulsive disorder) treatment, was invented by artificial intelligence through joint efforts of Exscientia (British start-up) and Sumitomo Dainippon Pharma (Japanese pharmaceutical firm). The drug development took a single year, while pharmaceutical companies usually spend about five years on similar projects. DSP-1181 was accepted for a human trial.

Industry 
The subsequent motive of large based health companies merging with other health companies, allow for greater health data accessibility. Greater health data may allow for more implementation of AI algorithms.

A large part of industry focus of implementation of AI in the healthcare sector is in the clinical decision support systems. As the amount of data increases, AI decision support systems become more efficient. Numerous companies are exploring the possibilities of  the incorporation of big data in the health care industry.

The following are examples of large companies that have contributed to AI algorithms for use in healthcare.

IBM 
IBM's Watson Oncology is in development at Memorial Sloan Kettering Cancer Center and Cleveland Clinic. IBM is also working with CVS Health on AI applications in chronic disease treatment and with Johnson & Johnson on analysis of scientific papers to find new connections for drug development. In May 2017, IBM and Rensselaer Polytechnic Institute began a joint project entitled Health Empowerment by Analytics, Learning and Semantics (HEALS), to explore using AI technology to enhance healthcare.

Microsoft
Microsoft's Hanover project, in partnership with Oregon Health & Science University's Knight Cancer Institute, analyzes medical research to predict the most effective cancer drug treatment options for patients. Other projects include medical image analysis of tumor progression and the development of programmable cells.

Google

Google's DeepMind platform is being used by the UK National Health Service to detect certain health risks through data collected via a mobile app. A second project with the NHS involves analysis of medical images collected from NHS patients to develop computer vision algorithms to detect cancerous tissues.

Intel

Intel's venture capital arm Intel Capital recently invested in startup Lumiata which uses AI to identify at-risk patients and develop care options.

Startups

Kheiron Medical developed deep learning software to detect breast cancers in mammograms.

Medvice provides real time medical advice to clients, who can access and store their Electronic Health Records (EHRs) over a decentralized blockchain. Medvice uses machine learning aided decision making to help physicians predict medical red flags (i.e. medical emergencies which require clinical assistance) before serving them. Predictive Medical Technologies uses intensive care unit data to identify patients likely to suffer cardiac incidents. Modernizing Medicine uses knowledge gathered from healthcare professionals as well as patient outcome data to recommend treatments. "Compassionate AI Lab" uses grid cell, place cell and path integration with machine learning for the navigation of blind people.  Nimblr.ai uses an A.I. Chatbot to connect scheduling EHR systems and automate the confirmation and scheduling of patients.

Infermedica's free mobile application Symptomate is the top-rated symptom checker in Google Play. The company also released the first AI-based voice assistant symptom checker for three major voice platforms: Amazon Alexa, Microsoft Cortana, and Google Assistant.

A team associated with the University of Arizona and backed by BPU Holdings began collaborating on a practical tool to monitor anxiety and delirium in hospital patients, particularly those with Dementia. The AI utilized in the new technology – Senior's Virtual Assistant – goes a step beyond and is programmed to simulate and understand human emotions (artificial emotional intelligence). Doctors working on the project have suggested that in addition to judging emotional states, the application can be used to provide companionship to patients in the form of small talk, soothing music, and even lighting adjustments to control anxiety.

Fractal Analytics has incubated Qure.ai which focuses on using deep learning and AI to improve radiology and speed up the analysis of diagnostic x-rays.

Other 

Digital consultant apps like Babylon Health's GP at Hand, Ada Health, and Your.MD use AI to give medical consultation based on personal medical history and common medical knowledge. Users report their symptoms into the app, which uses speech recognition to compare against a database of illnesses. Babylon then offers a recommended action, taking into account the user's medical history. Entrepreneurs in healthcare have been effectively using seven business model archetypes to take AI solution to the marketplace. These archetypes depend on the value generated for the target user (e.g. patient focus vs. healthcare provider and payer focus) and value capturing mechanisms (e.g. providing information or connecting stakeholders).

Implications

The use of AI is predicted to decrease medical costs as there will be more accuracy in diagnosis and better predictions in the treatment plan as well as more prevention of disease.

Other future uses for AI include Brain-computer Interfaces (BCI) which are predicted to help those with trouble moving, speaking or with a spinal cord injury. The BCIs will use AI to help these patients move and communicate by decoding neural activates.

As technology evolves and is implemented in more workplaces, many fear that their jobs will be replaced by robots or machines. The U.S. News Staff (2018) writes that in the near future, doctors who utilize AI will “win out” over the doctors who don't. AI will not replace healthcare workers but instead allow them more time for bed side cares. AI may avert healthcare worker burn out and cognitive overload. Overall, as Quan-Haase (2018) says, technology “extends to the accomplishment of societal goals, including higher levels of security, better means of communication over time and space, improved health care, and increased autonomy” (p. 43). As we adapt and utilize AI into our practice we can enhance our care to our patients resulting in greater outcomes for all.

Expanding care to developing nations
With an increase in the use of AI, more care may become available to those in developing nations. AI continues to expand in its abilities and as it is able to interpret radiology, it may be able to diagnose more people with the need for less doctors as there is a shortage in many of these nations.
The goal of AI is to teach others in the world, which will then lead to improved treatment, and eventually greater global health. Using AI in developing nations who do not have the resources will diminish the need for outsourcing and can use AI to improve patient care. For example, Natural language processing, and machine learning are being used for guiding cancer treatments in places such as Thailand, China and India. Researchers trained an AI application to use NLP to mine through patient records, and provide treatment. The ultimate decision made by the AI application agreed with expert decisions 90% of the time.

Regulation

While research on the use of AI in healthcare aims to validate its efficacy in improving patient outcomes before its broader adoption, its use may nonetheless introduce several new types of risk to patients and healthcare providers, such as algorithmic bias, Do not resuscitate implications, and other machine morality issues. These challenges of the clinical use of AI has brought upon potential need for regulations.

Currently no regulations exist specifically for the use of AI in healthcare. In May 2016, the White House announced its plan to host a series of workshops and formation of the National Science and Technology Council (NSTC) Subcommittee on Machine Learning and Artificial Intelligence. In October 2016, the group published The National Artificial Intelligence Research and Development Strategic Plan, outlining its proposed priorities for Federally-funded AI research and development (within government and academia). The report notes a strategic R&D plan for the subfield of health information technology is in development stages.

The only agency that has expressed concern is the FDA. Bakul Patel, the Associate Center Director for Digital Health of the FDA, is quoted saying in May 2017.“We're trying to get people who have hands-on development experience with a product's full life cycle. We already have some scientists who know artificial intelligence and machine learning, but we want complementary people who can look forward and see how this technology will evolve.”The joint ITU - WHO Focus Group on AI for Health has built a platform for the testing and benchmarking of AI applications in health domain. As of November 2018, eight use cases are being benchmarked, including assessing breast cancer risk from histopathological imagery, guiding anti-venom selection from snake images, and diagnosing skin lesions.