Ethical, Privacy, and Bias Challenges in AI Applications for Health Information Management
1
School Library and Media Technology, University of Ibadan, Ibadan, Nigeria
Popul. Med. 2026;8(Supplement Supplement 1):
ABSTRACT
ABSTRACT:
Artificial intelligence (AI) technologies are rapidly transforming health information management by enabling advanced analytics, personalised care, and operational efficiencies. However, the integration of AI in managing sensitive health data raises complex ethical issues around privacy, bias, transparency, and accountability. Despite AI’s promise, its deployment in health information systems risks undermining patient privacy, perpetuating algorithmic biases, and compromising ethical standards. Alsao, challenges such as opaque decision-making processes, unequal access to AI tools, and insufficient regulatory frameworks contribute to these concerns. Addressing these issues is critical to ensure equitable, trustworthy, and responsible AI utilisation in healthcare. This paper employs a systematic narrative review to synthesise current literature on AI ethics, privacy, and bias in health information management. Relevant peer-reviewed articles, policy frameworks, and case studies published from 2016 to 2025 were analysed to extract recurring themes, challenges, and proposed solutions. The review identifies key challenges including difficulties in maintaining patient data confidentiality, algorithmic bias leading to healthcare disparities, lack of transparent AI model interpretability, and blurred accountability among developers, providers, and institutions. Strategies, including implementing fairness-aware algorithms, enhancing model explainability, enforcing robust data governance policies, and fostering patient-centred consent mechanisms were identified. The study concludes that ethical deployment of AI in health information management requires holistic frameworks that balance innovation with privacy and fairness. The study recommends continuous bias auditing, transparent AI design, strengthened regulatory oversight, interdisciplinary collaboration, and empowering patients through informed consent as measures to promote equitable AI adoption, thereby enhancing patient trust and care quality in digital health ecosystems.
Artificial intelligence (AI) technologies are rapidly transforming health information management by enabling advanced analytics, personalised care, and operational efficiencies. However, the integration of AI in managing sensitive health data raises complex ethical issues around privacy, bias, transparency, and accountability. Despite AI’s promise, its deployment in health information systems risks undermining patient privacy, perpetuating algorithmic biases, and compromising ethical standards. Alsao, challenges such as opaque decision-making processes, unequal access to AI tools, and insufficient regulatory frameworks contribute to these concerns. Addressing these issues is critical to ensure equitable, trustworthy, and responsible AI utilisation in healthcare. This paper employs a systematic narrative review to synthesise current literature on AI ethics, privacy, and bias in health information management. Relevant peer-reviewed articles, policy frameworks, and case studies published from 2016 to 2025 were analysed to extract recurring themes, challenges, and proposed solutions. The review identifies key challenges including difficulties in maintaining patient data confidentiality, algorithmic bias leading to healthcare disparities, lack of transparent AI model interpretability, and blurred accountability among developers, providers, and institutions. Strategies, including implementing fairness-aware algorithms, enhancing model explainability, enforcing robust data governance policies, and fostering patient-centred consent mechanisms were identified. The study concludes that ethical deployment of AI in health information management requires holistic frameworks that balance innovation with privacy and fairness. The study recommends continuous bias auditing, transparent AI design, strengthened regulatory oversight, interdisciplinary collaboration, and empowering patients through informed consent as measures to promote equitable AI adoption, thereby enhancing patient trust and care quality in digital health ecosystems.
| eISSN: | 2654-1459 |
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