Healthcare systems are increasingly leveraging digital technologies, generating vast amounts of data that machine-learning algorithms can analyze to support diagnosis, prognosis, triage, and treatment of diseases. However, the integration of these algorithms into clinical practice is often impeded by insufficient evaluation across different environments. To address this, guidelines for evaluating machine learning tools in healthcare (ML4H) have been developed, focusing on assessing models for bias, interpretability, robustness, and potential failure modes. 

This study employed an ML4H audit framework across three use cases, revealing varied outcomes while underscoring the need for context-specific quality assessment and detailed evaluation. The paper recommends enhancements for future ML4H evaluation frameworks and explores the challenges associated with assessing bias, interpretability, and robustness. Standardized evaluation and reporting of ML4H quality are crucial for effectively integrating machine learning algorithms into medical practice.