A critical moment in machine learning in medicine: on reproducible and interpretable learning Machine learning (ML) has seen exponential growth in research, driven by advances in computational power and data availability. However, this rapid expansion has raised concerns about methodological rigor and reproducibility, especially in clinical settings where ML models can have severe consequences for patient health. The complexity of ML models has also compromised their interpretability, hindering their clinical adoption. This review discusses the importance of reproducibility and interpretability in ML, highlighting the challenges and potential solutions. Reproducibility refers to the ability of an independent group to reproduce results using the same data and code. It encompasses statistical and conceptual reproducibility, which are essential for clinical validity. Challenges include data privacy, small and noisy datasets, and limited generalizability of models. Solutions include shared data repositories, open science practices, and standardized reporting guidelines such as TRIPOD and SPRINT. Interpretability is crucial for clinical trust and decision-making. It involves understanding how models generate results and is often used interchangeably with explainability. ML models can be classified as interpretable or non-interpretable ("black box"). Balancing performance with interpretability is essential for clinical adoption. Techniques such as SHAP, LIME, UMAP, and Grad-CAM help explain model behavior. Simple models like decision trees and nomograms are more interpretable and suitable for medical applications. To address these issues, researchers should adopt best practices, ensure data sharing, and use standardized reporting guidelines. Dedicated ML reviewers and journals should promote rigorous standards. Additionally, methods like sensitivity analysis, heat maps, and forward/counterfactual simulations can assess model interpretability. Human-in-the-loop evaluations and feedback mechanisms are also important for validating explanations. The review emphasizes the need for improved standards, data sharing, and interpretability to ensure the reliability and credibility of ML in medicine. These steps are critical for the future development of the field and the safe application of ML in clinical practice.A critical moment in machine learning in medicine: on reproducible and interpretable learning Machine learning (ML) has seen exponential growth in research, driven by advances in computational power and data availability. However, this rapid expansion has raised concerns about methodological rigor and reproducibility, especially in clinical settings where ML models can have severe consequences for patient health. The complexity of ML models has also compromised their interpretability, hindering their clinical adoption. This review discusses the importance of reproducibility and interpretability in ML, highlighting the challenges and potential solutions. Reproducibility refers to the ability of an independent group to reproduce results using the same data and code. It encompasses statistical and conceptual reproducibility, which are essential for clinical validity. Challenges include data privacy, small and noisy datasets, and limited generalizability of models. Solutions include shared data repositories, open science practices, and standardized reporting guidelines such as TRIPOD and SPRINT. Interpretability is crucial for clinical trust and decision-making. It involves understanding how models generate results and is often used interchangeably with explainability. ML models can be classified as interpretable or non-interpretable ("black box"). Balancing performance with interpretability is essential for clinical adoption. Techniques such as SHAP, LIME, UMAP, and Grad-CAM help explain model behavior. Simple models like decision trees and nomograms are more interpretable and suitable for medical applications. To address these issues, researchers should adopt best practices, ensure data sharing, and use standardized reporting guidelines. Dedicated ML reviewers and journals should promote rigorous standards. Additionally, methods like sensitivity analysis, heat maps, and forward/counterfactual simulations can assess model interpretability. Human-in-the-loop evaluations and feedback mechanisms are also important for validating explanations. The review emphasizes the need for improved standards, data sharing, and interpretability to ensure the reliability and credibility of ML in medicine. These steps are critical for the future development of the field and the safe application of ML in clinical practice.