Bias-Mitigated AI as a Foundation for Resilient and Effective Health Systems.

Area of Science:

• Health Informatics
• Artificial Intelligence in Medicine
• Health Equity

Background:

• Artificial intelligence (AI) is transforming healthcare, yet biased algorithms risk perpetuating health disparities.
• Uneven data representation in AI training datasets limits accuracy and generalizability across diverse populations.
• Algorithmic bias in health AI must be recognized as a critical quality and safety concern.

Purpose of the Study:

• To frame algorithmic bias in health-related AI as a quality, safety, and governance challenge for health systems.
• To inform policymakers, regulators, health system leaders, and developers on operationalizing bias mitigation.
• To provide a regionally grounded policy perspective for the Americas, considering low- and middle-income settings.

Main Methods:

• Synthesizing existing scientific evidence and regulatory guidance.
• Outlining forms of algorithmic bias (representation, measurement, aggregation, deployment) across the AI lifecycle.
• Proposing a governance-oriented framework for bias mitigation from design to postmarket monitoring.

Main Results:

• Identified forms of algorithmic bias and their emergence within the AI lifecycle.
• Situated technical bias challenges within broader digital health and socioeconomic contexts.
• Developed a comprehensive governance framework for mitigating bias in health AI.

Conclusions:

• Algorithmic bias in health AI is a systemic quality and governance issue, not just a technical one.
• Effective bias mitigation requires a multi-stakeholder approach spanning the entire AI lifecycle.
• Embedding fairness as a measurable component of health system performance is essential for equitable AI deployment.