Reporting machine learning studies in clinical research: Practical guidelines for transparent and reproducible...

The expanding use of machine learning (ML) in clinical research has highlighted persistent methodological weaknesses that undermine the validity and interpretability of reported findings. Common issues include data leakage, inappropriate validation strategies, unstable model performance, and overinterpretation of predictive results. Importantly, strong predictive accuracy does not necessarily translate into clinical utility or biological insight, and poorly validated models may create a misleading impression of robustness. This editorial proposes a concise set of reporting guidelines to improve transparency, reproducibility, and the critical appraisal of ML-based analyses. The framework emphasizes key elements required for evaluation, including study objectives, data preprocessing, validation design, model development, performance assessment, and interpretation. Additional considerations address domain-specific challenges such as deep learning, missing data, and time-dependent structures. Rather than limiting methodological flexibility, these guidelines establish a minimal reporting standard that enables readers and reviewers to assess the credibility and generalizability of findings. By promoting clarity and completeness in reporting, they aim to reduce bias, improve reproducibility, and support the responsible integration of ML methods into clinical research.