The medical domain has presented key challenges to the AI community from the early days of AI research. It is not an exaggeration to say that this pioneering work, particularly in medical expert systems, and its undisputable successes, some in real-life settings, has helped both in restoring confidence in the promise of AI, that at some point was disturbed after its failure to deliver fully on the very ambitious initial goals that it had set, and in paving the way towards more viable paths harnessing the mechanization of knowledge and human expertise. AI in Medicine (AIM) is as old as AI itself, initially focusing on modelling human expertise, researching at the same time the cognitive processes involved in developing from a novice to an expert problem solver, as well as on intelligent tutoring systems for medical students and automated support for various clinical tasks. Over the years the initial focus on knowledge engineering has expanded to include ontologies and terminologies, natural language processing and text mining, guidelines and protocols, temporal information management, distributed and cooperative systems, uncertainty management, machine learning, image and signal processing and others. Recent interest focuses on medical analytics for healthcare intelligence. New challenges continuously arise, triggered from and/or triggering, important technological advances. The aim of this elective course is to familiarize students with the past, present and future of Artificial Intelligence in Medicine, illustrating the discussion with a number of case studies, and pinning down the human-centric and ethical aspects underlying the given applications.
- Teacher: Elpida Keravnou
- Teacher: Kalia Orfanou
- Teacher assistant: Pavlos Antoniou