The demographic transition of society and new technology contribute to exponential accumulation of micro-data in healthcare registers. Novel usage of artificially intelligent decision aids, applied on the Swedish register infrastructure, holds promises for improved quality and efficiency of healthcare. Focussing on cardiometabolic diseases, AIR Lund will critically assess the added value of machine learning compared to standard statistical approaches for predictions and decision aids in three specific settings:

1) prevention, where we hope to identify new groups of hidden high-risk individuals and new sets of modifiable risk factors
2) diagnosis, where we in emergency care hope to improve general risk assessment and diagnosis of acute coronary disease
3) prognosis, where we hope to improve long-term predictions and identify new risk patterns that forego adverse patient outcomes and high healthcare needs

AIR Lund will also analyse central ethical and legal dilemmas related to the use of AI and machine learning in clinical practice, and build on these results in our methodological work on fairness and transparency of AI tools.

AIR Lund represents a unique research environment at five different faculties at Lund University that collaborate closely with applied intelligent systems research at Halmstad University. We expect to bring substantial interdisciplinary novelty into register-based research, and at the same time to open up the Swedish register infrastructure to AI research.