Adaptive Thresholding in AI-CDSS: Dynamic Clinical Decision Boundaries

A narrative review synthesising 28 contemporary sources (2025–2026) on adaptive thresholding mechanisms in AI-based clinical decision support systems (AI-CDSS). What this presentation covers: • Why static decision thresholds fail when patient populations drift • Three architectural patterns: post-hoc recalibration, dynamic temporal risk modelling, and adaptive multi-agent architectures • Bayesian formalisation of threshold adaptation under distribution shift • The critical evidence gap: no prospective RCT has used missed-finding reduction as a primary endpoint • Regulatory implications under MDR and EU AI Act • A 6-point research agenda for the field Full-text article (open access, CC BY 4.0): https://doi.org/10.5281/zenodo.21946839 Author: Eduard Koshilko