Bias recognition and mitigation strategies in artificial intelligence healthcare applications

Fereshteh Hasanzadeh; Colin B. Josephson; G Waters; Demilade Adedinsewo; Zahra Azizi; James A. White

doi:10.1038/s41746-025-01503-7

March 11, 2025SHILAP Revista de lepidopterologíaOpen Access

Bias recognition and mitigation strategies in artificial intelligence healthcare applications

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Key Points

Examine the origins of algorithmic bias in healthcare artificial intelligence and identify strategies and stakeholder responsibilities necessary for equitable clinical application.
Conducted a narrative review of bias origins across the entire developmental lifecycle of clinical artificial intelligence models.
Evaluated systemic mitigation strategies spanning initial model conceptualization, clinical deployment, and long-term monitoring.
Identified that bias can infiltrate artificial intelligence tools at multiple developmental stages, exacerbating clinical and social disparities.
Demonstrated that mitigating bias requires continuous, structured interventions extending through post-deployment longitudinal surveillance.
Defined shared stakeholder obligations across clinical, technical, and regulatory domains to ensure fair and equitable healthcare outcomes.

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Implication

Review reveals sources of bias across artificial intelligence lifecycles in clinical practice, highlighting systematic mitigation strategies to prevent disparities and ensure equitable care.

Abstract

Artificial intelligence (AI) is delivering value across all aspects of clinical practice. However, bias may exacerbate healthcare disparities. This review examines the origins of bias in healthcare AI, strategies for mitigation, and responsibilities of relevant stakeholders towards achieving fair and equitable use. We highlight the importance of systematically identifying bias and engaging relevant mitigation activities throughout the AI model lifecycle, from model conception through to deployment and longitudinal surveillance.

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Cite This Study

Hasanzadeh et al. (2025) studied this question.

synapsesocial.com/papers/69d72ca55dca7d66cbbef1c6 https://doi.org/10.1038/s41746-025-01503-7

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Bias recognition and mitigation strategies in artificial intelligence healthcare applications

Key Points

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Implication

Abstract

Discussion

Cite This Study

Bias recognition and mitigation strategies in artificial intelligence healthcare applications

Key Points

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Implication

Abstract

Discussion

Cite This Study

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