A recent article in Nature takes a close look at bias in healthcare artificial intelligence (AI), outlining four key types—human, data, algorithmic, and deployment—and offering practical strategies for addressing them throughout the AI lifecycle.
Study: Bias recognition and mitigation strategies in artificial intelligence healthcare applications. Image Credit: Suri_Studio/Shutterstock.com
The authors emphasize fairness, equity, and explainability, providing a roadmap to reduce disparities and support more ethical AI implementation in clinical practice.
The Rise of AI in Healthcare—and the Risks That Come With It
AI-enabled medical devices are becoming increasingly common in healthcare, especially in specialties like radiology, cardiology, and neurology. As of May 2024, the US Food and Drug Administration (FDA) had approved 882 such devices, with 76 % focused on radiology alone. These tools excel at analyzing large datasets and spotting complex patterns—often delivering faster and more accurate results than traditional methods.
But alongside these benefits, serious concerns remain. Many of these AI systems operate as “black boxes,” meaning their decision-making processes are difficult to interpret or audit. This lack of transparency limits clinicians' ability to oversee or question AI outputs, raising flags about safety, biological plausibility, and ethical accountability. One of the most pressing issues is bias—systemic patterns in how AI makes decisions that can lead to unequal outcomes, especially for already vulnerable populations.
Despite growing attention from regulators like the FDA and World Health Organization (WHO), there’s still a lack of systematic approaches for identifying and mitigating bias across the AI lifecycle. The Nature article addresses this gap directly, offering a structured review of bias types, their root causes, and how they can be managed to support fairer, more trustworthy AI systems.
Mapping Out the Types—and Sources—of Bias
To build a clearer picture of where bias comes from and how it plays out in healthcare AI, the researchers analyzed 94 relevant studies published between 1993 and 2024. These were selected from an initial pool of 233 found via PubMed and Google Scholar.
Their review breaks bias into four categories:
- Human bias: including implicit, systemic, or confirmation bias during system design or training.
- Data bias: such as selection, representation, or measurement errors in training datasets.
- Algorithmic bias: including aggregation bias or issues stemming from how features are selected and weighted.
- Deployment bias: introduced during real-world use, such as through automation bias or feedback loops.
These biases often stem from unrepresentative data, weak model design, or excessive trust in AI recommendations without adequate human oversight. Alarmingly, the review found that half of existing healthcare AI models carry a high risk of bias due to incomplete data or design flaws—while only 20 % show a low risk.
The key takeaway is that without robust frameworks in place, even the most advanced AI tools risk reinforcing or amplifying disparities in care.
A Lifecycle Approach to Bias Mitigation
To address this, the authors propose managing bias at every stage of the AI model lifecycle—starting with conception and continuing through data collection, algorithm development, clinical deployment, and post-market monitoring.
Key strategies include:
- Involving diverse, multidisciplinary teams early in the design process.
- Ensuring data collection is representative and inclusive.
- Applying fairness-aware preprocessing techniques to correct imbalances before model training.
- Using fairness metrics and testing across subgroups during algorithm development.
- Monitoring for issues like concept drift or feedback bias after deployment, with mechanisms for ongoing evaluation and adjustment.
One case study cited in the paper highlights a cardiac MRI segmentation model that used stratified batch sampling and protected group models to reduce racial bias—an example of how these strategies can be applied in practice.
Still, the authors acknowledge real-world challenges: resource limitations, trade-offs between fairness and accuracy, and the difficulty of validating AI systems externally. That’s why they call for standardized frameworks and transparent evaluation processes to make ethical AI not just an ideal—but a standard.
Looking Ahead: Building Trust in AI-Driven Care
The integration of AI into healthcare is moving fast—but ethical concerns, especially around bias, are far from resolved. This study provides a clear-eyed look at how bias enters AI systems and how it can be addressed through thoughtful, end-to-end lifecycle management.
Key recommendations include involving diverse perspectives from the start, using inclusive data, incorporating fairness checks during development, and committing to ongoing monitoring after deployment. While technical and organizational hurdles remain, the authors emphasize that transparency, consistency, and accountability are critical to reducing disparities and fostering trust.
For the future, they suggest weaving AI ethics, diversity, and inclusion into both regulatory frameworks and medical education—laying the groundwork for more equitable, AI-powered care.
Journal Reference
Fereshteh Hasanzadeh, Josephson, C. B., Waters, G., Demilade Adedinsewo, Azizi, Z., & White, J. A. (2025). Bias recognition and mitigation strategies in artificial intelligence healthcare applications. Npj Digital Medicine, 8(1). DOI:10.1038/s41746-025-01503-7. https://www.nature.com/articles/s41746-025-01503-7
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