Artificial Intelligence for Medicine

Dr. Shai Ben-David earned his PhD in mathematics from the Hebrew University. Over the years, he has been a Professor at the Technion, Haifa, Israel and held visiting faculty positions at the Australian National University, Cornell University, ETH Zurich, and TTI Chicago. His research interests span a range of topics including logic, theory of distributed computation, machine learning, and complexity theory. He routinely serves as a senior area chair in the major machine learning conferences (NeurIPS, ICML, AISTATS, COLT and ALT) and won best paper awards in COLT 2006, NeurIPS 2018 and ALT 2023. He is a co-author of a popular textbook "Understanding Machine Learning: From theory to practice." Dr. Giuseppe Curigliano, MD PhD, is a Full Professor of Medical Oncology at the University of Milan and the Chief of the Clinical Division of Early Drug Development at European Institute of Oncology, Milan, Italy. Dr. Curigliano is an expert in the field of advanced drug development in solid tumors, with specific interest in breast cancer. He contributed to the development of many anticancer treatments currently available as standard of care in the treatment of multiple solid tumors. Dr Curigliano in 2022 was identified as Clarivate™ world’s most influential researchers. Dr Curigliano has contributed to over 670 peer-reviewed publications. Dr. David Koff, Professor Emeritus and past Chair of the Department of Radiology is the founder and director of MIIRCAM, the Medical Imaging Informatics Research Centre at McMaster University, where he oversees research initiatives on electronic transmission and workflow integration of medical images and AI applications in medical imaging. He co-developed the Canadian standards for lossy compression. He is a member of the IHE section of the RSNA's Medical Informatics committee and a co-founder of IHE Canada (Integrating the Healthcare Enterprise). Dr. Barbara Alicja Jereczek-Fossa is a Full Professor in Radiation Oncology at the University of Milan, Italy, and the Chair of the Division of Radiotherapy at the European Institute of Oncology, Milan. Her research expertise focuses on urological and breast malignancies, oligometastatic cancer, drug-radiation combinations and innovation in radiotherapy including proton therapy, stereotactic radiotherapy, AI and radiomics. Dr. Davide La Torre is a Full Professor in Applied Mathematics and Computer Science at SKEMA Business School in France. His areas of interest in research and instruction are Applied Mathematics, Artificial Intelligence, Mathematical Modeling, and Operations Research. He has previously held posts as a visiting and permanent university professor in Europe, Canada, the Middle East, Central Asia, and Australia. At various universities, he has held the positions of department chair and program director. He has contributed to more than 200 publications listed in Scopus. Dr. Gabriella Pravettoni is a Full Professor of Cognitive and Decision-Making Psychology at the University of Milan, where for several years she has been working on a new dimension of the doctor-patient relationship. She also serves as the Director of the Psycho-Oncology Division at the European Institute of Oncology, where she supervises clinical cases and develops and evaluates psychological treatments for cancer patients, putting the findings of several international research projects in which she is involved into clinical practice.

• 1. Artificial intelligence in cancer research and precision medicine2. Machine learning in computational pathology through self-supervised learning and vision transformers3. Artificial intelligence in small-molecule drug delivery4. AI/ML and drug repurposing in lung cancer: State of the art and potential roles for retinoids5. Artificial intelligence and digital worlds: New frontiers of integration between AI and other technological tools6. The dual path of the technology acceptance model: An application of machine learning cardiotocography in delivery rooms7. Artificial intelligence in diagnostic and predictive pathology8. Artificial intelligence in the oncology workflow: Applications, limitations, and future perspectives9. SOK: Application of machine learning models in child and youth mental health decision-making10. Cancer detection in hyperspectral imagery using artificial intelligence: Current trends and future directions11. Global research trends of Artificial Intelligence and Machine Learning applied in medicine: A bibliometric analysis (2012–2022)12. Ethics and regulations for AI in radiology13. The role of artificial intelligence in radiology and interventional oncology14. The multiomics revolution in the era of deep learning: Allies or enemies?15. Artificial intelligence in behavioral health economics: Considerations for designing behavioral studies16. Artificial intelligence and medicine: A psychological perspective on AI implementation in healthcare context17. AI for outcome prediction in Radiation Oncology: The present and the future18. Artificial intelligence in neurologic disease19. Should I trust this model? Explainability and the black box of artificial intelligence in medicine