Particularly in the domain of clinical decision support, digitalization efforts in healthcare have lagged far behind expectations. To be effective, any decision support needs to be adapted to both the data structure of the decision problem and the decision ecology of the end-user. Translational Data Science (TDS) is a novel approach to clinical decision support development in which the latest insights of the decision and data sciences are combined to quickly and efficiently move “from data to decision” (D2D). Dr. Niklas Keller will introduce the concepts and key-methods of TDS and present a use-case of the development of a decision support tool for post-operative patient allocation. The new approach kept all of the complexity of the decision problem at the “back-end” while maintaining a high degree of simplicity at the “front-end”. The resultant decision support has a high predictive accuracy, pays respect to the constraints of the decision ecology of the end-user, is action-oriented and can be easily integrated in various clinical settings as a laminated pocket card.
AI in medicine and healthcare is getting great traction and attention but often because of bold, and sometimes unjustified, claims magnified by media coverage. In this talk, Hector will cover some of the very important challenges and limitations of AI in medicine and healthcare and how to take a responsible approach using approaches from neurosymbolic computation as opposed to simply statistical machine learning. He will introduce the work that we have done and continue doing at the Karolinska Institute and Oxford Immune Algorithmics to make the introduction of AI in medicine and healthcare more responsible.