What makes clinical machine learning fair? A practical ethics framework.

Area of Science:

• Medical Informatics
• Artificial Intelligence in Healthcare
• Clinical Decision Support Systems

Background:

• Machine learning (ML) offers significant potential to enhance medical decision-making, accuracy, and patient outcomes.
• The integration of ML into clinical workflows raises ethical concerns, particularly regarding algorithmic bias.
• Addressing bias is crucial for the responsible and equitable deployment of ML in medicine.

Purpose of the Study:

• To introduce and discuss a practical ethics framework for identifying, measuring, and mitigating bias in clinical machine learning models.
• To provide a proportionate approach to ML bias that balances ethical justification with technical feasibility.
• To enable ethically robust and transparent decision-making in the design and application of clinical ML models.

Main Methods:

• Inductive generation of a practical ethics framework through normative analysis of challenges in developing a clinical ML model.
• Detailed examination of a proportionate approach to ML bias, considering ethical demands and technical limitations.
• Case study analysis of practical challenges in clinical ML model development.

Main Results:

• The developed framework effectively identifies, measures, and addresses bias in clinical ML models.
• The framework facilitates improved fairness in both model performance and health outcomes.
• It supports ethically sound and transparent decision-making regarding ML bias mitigation.

Conclusions:

• The proposed ethics framework enhances accountability for developers and clinical users of ML models.
• It offers a practical solution for managing algorithmic bias in healthcare settings.
• Implementing this framework can lead to more equitable and reliable AI-driven medical tools.