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Biases in Artificial Intelligence Application in Pain Medicine
Oranicha Jumreornvong1, Aliza M Perez1, Brian Malave1
1Department of Human Performance and Rehabilitation, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Journal of Pain Research
|March 5, 2025
Summary
Artificial Intelligence (AI) in pain management can be biased by sex, race, and socioeconomic status (SES), worsening health disparities. This review proposes fairness-aware AI techniques and diverse collaboration to ensure equitable pain treatment.
Area of Science:
- Medical Informatics
- Health Equity
- Artificial Intelligence
Background:
- Artificial Intelligence (AI) offers potential for personalized pain treatment and improved clinical decisions.
- Biases in AI, stemming from sex, race, socioeconomic status (SES), and statistical methods, risk exacerbating existing health disparities in pain management.
Purpose of the Study:
- To review biases in AI applied to pain management.
- To propose strategies for mitigating these biases and promoting equitable AI in healthcare.
Main Methods:
- A narrative review methodology was employed.
- Literature search conducted across PubMed, Google Scholar, and PsycINFO for AI in pain management and bias sources.
Main Results:
- Sex and racial biases arise from societal stereotypes, underrepresentation, and systemic inequities, leading to inaccurate pain assessments.
- Socioeconomic status (SES) biases result from unequal healthcare access and incomplete data, increasing prediction errors.
- Statistical biases, including sampling and measurement issues, further compromise AI algorithm reliability.
Conclusions:
- Fairness-aware AI techniques (e.g., reweighting, adversarial debiasing) are recommended to minimize bias.
- Incorporating diverse perspectives (patients, clinicians, policymakers) is crucial for developing fair and interpretable AI.
- Continuous monitoring and inclusive collaboration are essential for equitable AI in pain management.
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