What question did this study set out to answer?

Examine the origins of algorithmic bias in healthcare artificial intelligence and identify strategies and stakeholder responsibilities necessary for equitable clinical application.

March 11, 2025SHILAP Revista de lepidopterologíaOpen Access

Bias recognition and mitigation strategies in artificial intelligence healthcare applications

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Authors

FHFereshteh Hasanzadeh CJColin B. Josephson GWG Waters

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Overview

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

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.

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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March 11, 2025SHILAP Revista de lepidopterologíaOpen Access

Bias recognition and mitigation strategies in artificial intelligence healthcare applications

View Full Paper

Ask AI

Bookmark

Authors

FHFereshteh Hasanzadeh CJColin B. Josephson GWG Waters

Discussion

Member takes

Overview

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

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.

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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