A translational perspective towards clinical AI fairness.

Area of Science:

• Medical Informatics
• Artificial Intelligence Ethics
• Clinical Decision Support

Background:

• AI demonstrates data insight extraction but raises fairness concerns in high-stakes fields like healthcare.
• Existing AI fairness metrics and mitigation methods may not align with clinical contexts.
• Clinical applications require careful examination of sensitive variables and ethical implications of fairness metrics.

Purpose of the Study:

• To address the inadequacies of current AI fairness approaches in clinical contexts.
• To propose an equity-based framework for clinical AI fairness.
• To highlight the necessity of multidisciplinary collaboration for fair AI in healthcare.

Main Methods:

• Critical analysis of standard AI fairness definitions and measurements in the context of healthcare.
• Examination of ethical considerations and potential conflicts in fairness metrics.
• Emphasis on the role of clinical justification for subgroup performance differences.

Main Results:

• Standard AI fairness definitions (equality) are often misaligned with clinical realities.
• Clinical AI fairness should prioritize equity over strict equality.
• Clinical feedback and clinician involvement are crucial for developing fair AI models.

Conclusions:

• Adapting AI fairness to healthcare necessitates a shift from "equality" to "equity."
• Multidisciplinary collaboration among AI researchers, clinicians, and ethicists is essential.
• Bridging the gap between technical AI fairness and clinical considerations is vital for real-world benefits.