Show Your Work: Responsible Model Reporting in Health Care Artificial Intelligence

Area of Science:

• Health Informatics
• Machine Learning
• Artificial Intelligence in Healthcare

Background:

• Standardized model reporting is essential for developing and deploying machine learning (ML) models in healthcare.
• Effective reporting involves sharing performance metrics and metadata for robust model evaluation.
• Addressing concerns like explainability, transparency, fairness, and generalizability is vital for AI in healthcare.

Purpose of the Study:

• To emphasize the importance of standardized and thorough model reporting in the healthcare machine learning lifecycle.
• To highlight how comprehensive reporting facilitates trust and responsible AI deployment.
• To underscore the role of physician involvement in ensuring clinical relevance and safety.

Main Methods:

• Review of best practices in machine learning model reporting.
• Analysis of components of thorough model reporting, including performance metrics and metadata.
• Discussion of the integration of responsible reporting throughout the model development lifecycle.

Main Results:

• Standardized reporting enhances model evaluation by providing necessary context.
• Thorough reporting directly addresses critical concerns regarding AI in healthcare.
• Open communication via reporting supports stakeholder understanding and trust.
• Physician engagement ensures clinical validity and mitigates potential negative consequences.

Conclusions:

• Responsible model reporting is integral to the successful and ethical deployment of machine learning in healthcare.
• Comprehensive reporting fosters transparency, fairness, and generalizability, building confidence in AI healthcare solutions.
• Collaborative development involving clinicians is key to aligning AI models with patient care needs.