Intelligent Medical Systems (IMEDS)

Motivation. The IMEDS group develops new AI methods for medical applications. The research that we are conducting is motivated by the following challenges that we either already face with medical AI, or will be facing in the very near future:

• Today's AI does not treat patients, doctors, and hospital personnel as a part of the decision-making workflow.
• Developers do not (and cannot) optimize the right healthcare metrics: patient outcomes, as well as diagnostic and treatment costs.
• Methods that we have on the machine learning side have achieved success in tasks that are closer to automated data analysis and pattern recognition than to actual automatic decision-making.

So, how can we address the defined challenges? I envision that in order for patients to gain benefit from AI systems, we need to develop new methods on the Machine Learning side that

• Are multimodal
• Use as little labeled data as possible
• Predict how uncertain they are in their predictions
• Interact with humans and other AI systems

Vision. Powerful methods are good to have, but this is still not enough, as basic Machine Learning technology is merely an enabler. I believe that in order to see substantial improvements in health and care, we need to build new hospital processes, which are AI-tailored by design. The future AI-tailored hospitals (FAITH) will comprise AI systems designed for all major stakeholders in a hospital workflow (including the hospital itself), and those systems shall be seen as a new type of medical software. In practice, I foresee the following implementation of FAITH:

• Patient’s AI: Personalized (to patients) diagnostic tools, and adaptive behavioral intervention systems.
• Doctor’s AI: Personalized (to doctors and patients) prognostic and treatment recommendation tools.
• Hospital’s AI: Productivity tools.