Artificial Intelligence in Medicine publishes original articles from a wide variety of interdisciplinary perspectives concerning the theory and practice of artificial intelligence (AI) in medicine, human biology, and health care.

Particular attention is given to:

• AI-based clinical decision making
• Medical knowledge engineering
• Knowledge-based and agent-based systems
• Computational intelligence in bio- and clinical medicine
• Intelligent medical information systems
• AI in medical education
• Intelligent devices and instruments
• Automated reasoning and metareasoning in medicine
• Methodological, philosophical, ethical, and social issues of AI in medicine

AIIM features:

• Original research contributions
• Methodological reviews
• Survey papers
• Special issue articles
• Position papers
• Historical perspectives
• Editorials
• Guest editorials
• Letters to the editor
• Book reviews

Special Issue on Large language Models for Medicine
Submission Date: 2024-04-02

Large language models (LLMs) have a great potential to make a positive impact in the medical domain. They can be used in a clinical context both as interactive decision support tools for medical experts, assist in triage processes, enhance symptom collection, and streamline administrative functions like the consolidation of patient notes and discharge documents. At the same time, the medical domain poses more difficult challenges to LLMs than many other domains, among others because inaccuracies can have disastrous consequences. To embrace the challenges and opportunities in designing, validating and deploying LLMs in a medical context, this special issue seeks submissions of scientific findings from both academia and healthcare industry that present fundamental theory, techniques, practical experiences of LLMs in medicine as well as roadmaps.

Guest editors:

Grigoris Antoniou is Research Professor of Artificial Intelligence at the University of Huddersfield, UK, and Leibniz University Hannover, Germany. His research activity spans over knowledge representation, semantic web technologies, hybrid AI learning and AI for medicine. He has published over 300 scientific papers that have attracted over 14,000 citations. Among others, he was guest editor of a special issue of Artificial Intelligence in Medicine on Medical Analytics for Healthcare Intelligence in 2021. He is Fellow of IEEE, European AI Society and Asia-Pacific AI Association.Email: g.antoniou@hud.ac.uk
Keno Bressem is a board certified radiologist at Charité Universitätsmedizin Berlin, Germany and Digital Clinician Scientist at the Berlin Institute of Health, Germany. His research activity focusses on applied AI in medicine including computer vision and natural language processing. He is coordinator of the EU-funded project COMFORT (https://comfort-ai.eu/) a multinational initiative that focusses on the development of AI solutions of urologic cancers. He is member of the Radiologic societies of North America, Europe and Germany.

Frank van Harmelen is professor of Knowledge Representation and Reasoning at the Vrije Universiteit Amsterdam. As one of the early researchers in semantic web technologies (currently known as linked data and knowledge graphs), he co-defined the Web ontology language OWL, which has become a worldwide standard and is in wide academic and commercial use. He is principal investigator of the Hybrid Intelligence Centre (https://hybrid-intelligence-centre.org ), a 20m€, 10 year research project into AI systems that collaborate with people instead of replacing them. He is a fellow of the European AI Society and of the Asia-Pacific AI Association. He was elected a member of the Academia Europae, and of the Royal Netherlands Society of Sciences and Humanities (KNAW). Among other journals, he serves on the editorial board of the AI in Medicine journal.

Alexander Löser is Professor for Data Science and Text-Mining at the Berliner Hochschule für Technik and Director of the Data Science Research Center. His research interests lie at the intersection of natural language processing and machine learning, in particular for clinical applications. He is expert for LLMs in the ‘Plattform Lernende Systeme,‘ a platform of leading AI experts sponsored by BMBF, the German Federal Ministry of Education and Research. Alexander has a well-established track record of innovation and technology transfer. He worked as an independent consultant with eBay, IBM, Zalando, MunichRe, SpringerNature, Fresenius, Krohne, Babbel, among others. Over the time he helped these organizations to create six data platforms with more than 45 data products.

Wolfgang Nejdl is Professor of Computer Science at Leibniz University. He heads the L3S Research Center, www.L3S.de, as well as the Data Science Institute / Knowledge Based Systems, and does research in the areas of Information Retrieval, Artificial Intelligence, Social and Semantic Web, Digital Libraries and Technology Enhanced Learning. He was PI of the ERC Advanced Grant ALEXANDRIA, from 2014 - 2019, working on foundations for temporal retrieval, exploration and analytics in Web archives. Current projects include NoBIAS, SoBigData++, and the International Leibniz Future Lab on Artificial Intelligence, with a special focus on personalized medicine.Wolfgang Nejdl, www.kbs.uni-hannover.de/~nejdl, has published more than 430 scientific articles, with an h-index (based on Google Scholar) of 78.

Special issue information:

The topics of this special issue include, but are not limited to:

Novel text resources for training clinical LLMs
Translating doctor-to-patient language
Training and refining LLMs for medical education
Training and refining LLMs for medical research
Local language models
Combining LLMs and medical knowledge graphs
Accuracy and recency of LLMs for medical applications
New evaluation paradigms for medical LLMs
Explanations of LLMs for medical applications
Addressing multi-linguality requirements in clinical LLMs
Real-time Web access for medical LLMs
Addressing bias in medical LLMs
Ethical and legal aspects of medical LLMs

We encourage authors to publish training data and models on platforms such as at Hugging Face or Github.