Calibrating CONSORT-AI with FAIR Principles to enhance reproducibility in AI-driven clinical trials

Abstract

Abstract Artificial intelligence (AI) is increasingly embedded in clinical trials, yet poor reproducibility remains a critical barrier to trustworthy and transparent research. In this study, we propose a structured calibration of the CONSORT-AI reporting guideline using the FAIR (Findable, Accessible, Interoperable, Reusable) principles. We introduce the application of CALIFRAME, a framework designed to evaluate and align existing medical AI reporting standards with FAIR-compliant practices. Applying CALIFRAME to the CONSORT-AI checklist reveals specific gaps in data and code sharing, metadata use, and accessibility practices in current AI-driven clinical trials. Our results underscore the need for standardized metadata, clear licensing, and stakeholder-inclusive design in medical AI reporting. We demonstrate that FAIR- oriented calibration of reporting guidelines can bridge the reproducibility gap and support more transparent, efficient, and reusable AI interventions in healthcare. This work advocates for a shift toward reproducibility as a foundation for trustworthy AI in clinical research.

Ähnliche Arbeiten