Detecting and Resolving Bias in Healthcare AI
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V6I2P110Keywords:
Healthcare Artificial Intelligence, Algorithmic Bias, Fairness In Machine Learning, Ethical AI, Data Imbalance, Bias Detection, Bias Mitigation, Explainable AI (XAI), Predictive Modeling, Clinical Decision Support Systems, Health Equity, Transparency, Accountability, Adversarial Debiasing, Fairness Metrics, Model Interpretability, Responsible AI, Bias Resolution Framework, Federated Learning, Regulatory Compliance In AIAbstract
Artificial intelligence (AI) is changing healthcare in a huge way by making it easier to get many quick diagnoses, personalized treatment & predictive analytics. However, it also has the huge problem of bias. Healthcare AI can become biased through datasets that aren't adequately represented, inaccurate their information labeling & systemic disparities in the healthcare systems itself. This can lead to disproportionate recommendations, inadequate diagnoses, or not a sufficient representation of these specific groups. These biases make clinical accuracy very less reliable & make health disparities worse, particularly for those who are poor or disadvantaged. Recognizing & correcting this kind of prejudice is essential to ensuring that these AI systems lead to fair healthcare outcomes. This paper investigates several bias detection approaches, including fairness audits, model interpretability analysis & statistical parity evaluations, as well as mitigation measures such as data rebalancing, adversarial de-biasing & continuous model retraining using these diverse datasets. It reiterates the need for clear conceptual governance, ethical evaluation protocols & clinical involvement during their AI validation. Research indicates that a comprehensive approach integrating technological improvements with ethical & governmental oversight may significantly reduce their algorithmic prejudice and enhance confidence regarding healthcare AI systems. In the end, fighting prejudice is not only a technological problem; it is also a moral one. AI should improve the decision-making process in a manner that is equitable, reliable & transparent to all types of patients as well
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References
[1] Norori, Natalia, et al. "Addressing bias in big data and AI for health care: A call for open science." Patterns 2.10 (2021).
[2] Nazer, Lama H., et al. "Bias in artificial intelligence algorithms and recommendations for mitigation." PLOS digital health 2.6 (2023): e0000278.
[3] Tejani, Ali S., et al. "Detecting common sources of ai bias: Questions to ask when procuring an ai solution." Radiology 307.3 (2023): e230580.
[4] Koçak, Burak, et al. "Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects." Diagnostic and interventional radiology 31.2 (2025): 75.
[5] Vajpayee, Ashutosh S., and Deepak Khobragade. "The Problem Of Data Bias In Healthcare AI." 2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI). IEEE, 2024.
[6] Jain, Anjali, et al. "Awareness of racial and ethnic bias and potential solutions to address bias with use of health care algorithms." JAMA Health Forum. Vol. 4. No. 6. American Medical Association, 2023.
[7] Chinta, Sribala Vidyadhari, et al. "AI-Driven Healthcare: A Review on Ensuring Fairness and Mitigating Bias." arXiv preprint arXiv:2407.19655 (2024).
[8] Byrne, Matthew D. "Reducing bias in healthcare artificial intelligence." Journal of PeriAnesthesia Nursing 36.3 (2021): 313-316.
[9] Alexiev, Christopher. Interpretable and Automated Bias Detection for AI in Healthcare. Diss. Massachusetts Institute of Technology, 2024.
[10] Chinta, Sribala Vidyadhari, et al. "Ai-driven healthcare: A survey on ensuring fairness and mitigating bias." arXiv preprint arXiv:2407.19655 (2024).
[11] Panch, Trishan, Heather Mattie, and Rifat Atun. "Artificial intelligence and algorithmic bias: implications for health systems." Journal of global health 9.2 (2019): 020318.
[12] Chen, Feng, et al. "Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models." Journal of the American Medical Informatics Association 31.5 (2024): 1172-1183.
[13] Chen, Richard J., et al. "Algorithmic fairness in artificial intelligence for medicine and healthcare." Nature biomedical engineering 7.6 (2023): 719-742.
[14] Kumar, Ashish, and Divya Singh. "Analysis of AI-Bias in Modern Healthcare Systems." Artificial Intelligence in Modern Healthcare System. Singapore: Springer Nature Singapore, 2025. 327-350.
[15] Schwartz, Reva, et al. Towards a standard for identifying and managing bias in artificial intelligence. Vol. 3. Gaithersburg, MD: US Department of Commerce, National Institute of Standards and Technology, 2022.
