Clinical Decision Support

The integration of AI algorithms into clinical routine has so far been limited. CAIMed investigates methodological challenges in the evaluation of informatics diagnostics, the synthesis of machine-learned and evidence-based decision models, the handling of continuously learning algorithms and the integration of AI models into clinical processes, including hybrid models with ontological domain knowledge ( Human-centered AI) and explainability components.

The evaluation is based on studies with users, usability tests with prototypes, prospective diagnostic studies, simulated randomized controlled trials (RCTs), synthetic data and comparative clinical studies in the field of infectious diseases in pediatrics. Semantically integrated, heterogeneous medical data is used, in particular from the Medical Data Integration Center of the MHH. The creation of hybrid models and their interdisciplinary interpretation is carried out in collaboration with the Human-Centered AI group. During evaluation, the group cooperates closely with the  Statistical Evidence in AI Systems.

Team

Mentor

Prof. Dr. Philipp Beerbaum, MHH

Mentor