AI in Medicine, Covid 19 and Springer Nature's Open Access Agreement
Dear readers, Let's say something about applications of AI in Medicine first. They include intelligent interactive monitoring of patient's environment and needs, intelligent interfaces supporting access to healthcare services, and patient tailored decision support. Just to name a few. In addition, they include decision support systems especially for the doctor, which model the diagnostic reasoning and decision making of medical experts. In recent years, prominent examples also include machine learning in medical imaging; deep learning for identifying metastatic breast cancer on images seems promising. Or in non imaging domains, massively multitask networks for drug discovery. This means, AI is interdisciplinary in medicine, a real general purpose technology around images, texts, and biodata. At least four application fields become an ally and seem promising in the fight against Covid 19. (Although AI has not yet made an impact. The community, you, have taken up the challenge. First, medical imaging might help to spot Covid 19 in x rays. This recent development might however never result into a production ready solution, Berlin Charite's lab solution with monovettes just seem more appropriate. The second application field is gene sequencing. Commercial companies such as DeepMind offer data and contribute to the scientific effort by releasing structure predictions of several understudied proteins associated with Covid 19. The third application field is language technology and text mining. The Allen institute for AI creates a Kaggle challenge, issuing a call to action to AI experts to develop text mining solutions. The Covid 19 Open Research (CORD 19) dataset represents an extensive machine readable coronavirus literature collection. The fourth application field is disaster management. Only recently, BMBF announces a call about national disaster management, focusing on natural disasters such as flood. Covid 19 management would be welcome. Coming to the two main points, to use AI methods to a larger extent, we need a systematic collection of patient information and medical literature in a digital format, and machine learning solutions for end users. Digital records can be shared across different healthcare settings, store data accurately, and capture the state of a patient across time. In addition, open source software and open access scientific papers and books around AI and medicine have to be made available. Just at the right time, we are happy to announce Springer Nature's Open Access Agreement (read more in the News section of the KI Journal) if you are a corresponding author affiliated with a German university or research institution, you are entitled to publish open access in our KI Journal with fees covered by the German DEAL agreement. This means everyone in the German AI research community can, from now on, publish open access in our KI Journal for free, with Scopus Index! Now to the current special issue on Interactive Machine Learning, which could be defined as the design and implementation of algorithms and intelligent user interface frameworks that facilitate machine learning with the help of human interaction. With the convergence of artificial intelligence and machine learning, and the aforementioned need of machine learning tools for medical end users (cf. the ongoing coronavirus pandemic) I am happy we have such a strong topic, i.e. challenges in interactive machine learning (and explainable AI) in this special issue. My thanks go out to the guest editors Stefano Teso and Oliver Hinz, the liaison editor Kristian Kersting, the reviewers of these papers, and Springer Nature. Stay healthy, Daniel Sonntag Daniel Sonntag daniel.sonntag@dfki.de
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