AI-assisted scoping review of code sharing in clinical prediction model research.

Abstract

Clinical prediction models are increasingly deployed to support diagnostic and prognostic decisions, making reproducibility essential for assessing their reliability and generalizability. Analytical code supports independent assessment of these processes, yet its availability in the literature remains limited. This scoping review quantifies current practices in sharing analytical code to inform the development of TRIPOD-Code, a reporting guideline for code availability and reproducibility. Here a large-language-model-assisted pipeline was developed to screen articles citing TRIPOD or TRIPOD+AI, extract repository links and assess retrieved repositories against 14 predefined reproducibility-related features. Among 3,967 articles, 482 (12.2%) included code-sharing statements. Sharing prevalence varied widely by journal and country. Repository assessment showed substantial heterogeneity in reproducibility features. These findings underscore the need for clearer expectations beyond code availability, including documentation, dependency specification and executable structure. Strengthening these practices may improve the usability of clinical prediction model studies and support their deployment in real-world clinical settings.