Machine learning and deep networks are transforming the field of medicine in exciting yet hitherto unknown ways, all the way from diagnosis to treatment. Our goal is to foster communication among AI researchers in different groups broadly working in this domain. We want our exchange sessions to be a place where we can critically examine key topics in AI and medicine, generate fresh ideas and discussion around their intersection with problems we’re working on, and most importantly, learn from each other and develop new collaborations and synergies in our work.
We will be having weekly sessions (on Thursdays, 1-2pm PT) where our invited guests will present their work or a journal club paper followed by an interactive discussion and Q&A among all participants. Sessions will be recorded or livestreamed based on presenters’ consent and the recordings will be available for viewership later. Check our About page for more details.
Check out our Upcoming Speakers Schedule and view details of our Past Speakers.
Currently, three medical AI groups spanning multiple universities and institutions are participating in the MedAI Group Exchange Sessions, each having distinct backgrounds and expertise:
- Rubin Lab (Stanford University)
- Banerjee Lab (Mayo Clinic, Arizona State University, Emory University)
- Summers Lab (National Institute of Health (NIH))
Topics of interest to these groups include but are not limited to:
- Computer vision
- Natural language Processing
- AI-based clinical decision making;
- Medical knowledge engineering;
- Knowledge-based and agent-based systems;
- Computational intelligence in bio- and clinical medicine;
- Intelligent and process-aware information systems in healthcare and medicine;
- Natural language processing in medicine;
- Data analytics and mining for biomedical decision support;
- New computational platforms and models for biomedicine;
- Intelligent exploitation of heterogeneous data sources aimed at supporting decision-based and data-intensive clinical tasks;
- Intelligent devices and instruments;
- Automated reasoning and meta-reasoning in medicine;
- Machine learning in medicine, medically-oriented human biology, and healthcare;
- AI and data science in medicine, medically-oriented human biology, and healthcare;
- AI-based modeling and management of healthcare pathways and clinical guidelines;
- Models and systems for AI-based population health;
- AI in medical and healthcare education;
- Methodological, philosophical, ethical, and social issues of AI in healthcare, medically-oriented human biology, and medicine.