Artificial Intelligence in Healthcare: Legal, Ethical, and Compliance Implications for Responsible Decision-making
Abstract
The delivery of medical services has changed as a result of the quick integration of artificial intelligence (AI) into global healthcare systems. Still, it has also brought up difficult ethical, legal, and regulatory issues. This study examined the ramifications in relation to Nigeria's healthcare system, highlighting the need for accountable and open decision-making. Two hundred participants, including IT specialists, policy experts, and healthcare practitioners in Lagos, Abuja, and Port Harcourt, were given standardized questionnaires as part of a quantitative study design. Descriptive statistics were used to evaluate 182 valid replies out of the total. According to the findings, Nigeria's use of AI technologies is still hampered by lax legislative frameworks, unclear ethical standards, and insufficient data protection compliance, even though these technologies are being used more and more for administrative efficiency and diagnostic help. Concerns around algorithmic bias, patient privacy, and the lack of clear accountability for AI-related failures were raised by respondents. The study concludes that strong ethical governance, legally binding standards, and ongoing human oversight are necessary for the proper application of AI in healthcare. Policy implications emphasize the pressing need for professional training on AI ethics, enhanced institutional capacity, and a national AI health strategy. By integrating AI ethics and compliance into the Nigerian healthcare environment, the study contributes to the existing body of literature. It also provides policymakers with a framework for striking a balance between technical innovation and moral and legal accountability.
Keywords
Artificial Intelligence (AI); Healthcare; Ethics; Legal Compliance; Responsible Decision-Making; Data Protection; Nigeria; AI Governance; Medical Technology; Health Policy.
References
Adebayo, S. A. (2020). Legal Implications of Artificial Intelligence in Cross Border Contracts. International Journal of Law and Societal Studies, 1(1), 98-103. Adeniyi, A. C., & Okusi, O. (2024). Ethical AI Governance, Financing, and Human Well-Being in the 21st Century. African Journal of Humanities and Contemporary Education Research, 17(1), 193-205. Afolabi, A. (2024). Ethical Issues in Artificial Intelligence Adoption in African Higher Education Institutions in Nigeria. African Journal of Information and Knowledge Management, 3(2), 22-33. Alaran, M., Lawal, S. K., Jiya, M. H., & Egya, S. A. (2025). Challenges and opportunities of artificial intelligence in African health space. Digital Health, 11(3). Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020). Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Medical Informatics and Decision, 20(1), 1-9. Anamoji, G. S. (2024). Ethical and Regulatory Framework of Artificial Inteligence (A.I) in Nigeria: The Dilemna of Global Adaptation for Sustainable Growth. International Journal of Innovative Social Sciences & Humanities Research, 12(3), 55-65. Beauchamp, T. L., & Childress, J. F. (2019). Principles of Biomedical Ethics. Oxford University Press. Benjamens, S., Dhunnoo, P., & Mesko, B. (2020). The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. Digital Medicine, 3(1), 1-8. Calo, R. (2019). Artificial Intelligence Policy: A Primer and Roadmap. UC Davis Law Review, 51,399. Creswell, J. W., & Creswell, D. J. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approach (Fifth ed.). California: Sage Publication. Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3 ed.). California: Sage Publication. Davenport, T. H., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Hospital Journal, 6(2), 94-98. Dignum, V. (2022). Responsible Artificial Intelligence: Recommendations and Lessons Learned. In D. O. Eke, K. Wakunuma, & A. Simisola, Responsible Al in Africa (pp. 195-213). Palgrave Macmillan. Ehirim, U. G. (2025). Artificial Intelligence and Healthcare Delivery in Nigeria: Legal and Ethical Dimensions of Patients' Rights to Safety. Indonesian Journal of Sharia and Socio-Legal Studies, 1(1), 47-71. Esteva, A., Robicquet, A., Ramsundar, B., & Kuleshov, V. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24-29. European Commission. (2023, September 20). The Artificial Intelligence Act. Retrieved from European Union Publication: https://op.europa.eu/en/web/general-publications/ai-2025 Ezeaka, N. B. (2024). Artificial Intelligence (AI) and Health Communication Policy in Nigeria: Challenges and Prospects. Journal of Advanced Research and Multidisciplinary Studies, 4(4), 141-149. Faruk, N., Surajudeen-Bankole, N., Abudulkarim, A., Oloyede, A., Abiodun, O., Bello, O. W., ... Edoh, T. O. (2020). Rural Healthcare Delivery in Sub-Saharan Africa: An ICT-Driven Approach. International Journal of Healthcare Information System and Informatics, 15(2), 21. Floridi, L., & Cowls, J. (2019). A Unified Framework of Five Principles for Al in Society. Harvard Data Science Review, 1(1), 1-13. Gerke, S., Minssen, T., & Cohen, G. (2020). Ethical and legal challenges of artificial intelligence-driven healthcare. In A. Bohr, & K. Memarzadeh, Artificial Intelligence in Healthcare (pp. 295-336). Academic Press. Ghassemi, M., Oakden-Rayner, L., & Beam, A. B. (2021). The false hope of current approaches to explainable artificial intelligence in health care. The Lancet Digital Health, 3(11), 745-750. Goodfellow, I., Bengio, Y., & Courville, A. (2017). Deep Learning. The MIT Press. Guest, G., Namey, E. E., & Mitchell, M. L. (2017). Collecting Qualitative Data: A Field Manual for Applied Research. Sage. Hair, J. F., Babin, B. J., Anderson, R. E., & Black, W. C. (2019). Multivariate Data Analysis. Pearson Prentice. Hui, A., Ahn, S., Lye, C. T., & Deng, J. (2021). Ethical Challenges of Artificial Intelligence in Health Care: A Narrative Review. Ethics in Biology, Engineering & Medicine - An International Journal, 12(1), 55-71. Ismail, O., & Ahmad, N. (2025). Ethical and Governance Frameworks for Artificial Intelligence: A Systematic Literature Review. International Journal of Interactive Mobile Technology, 19(14), 121-136. Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of Al ethics guidelines. Nature Machine Intelligence, 1(2). Kellermann, A. L., & Jones, S. S. (2015). What It Will Take To Achieve The As-Yet-Unfulfilled Promises Of Health Information Technology. Health Affairs, 32(1), 63-68. Khalid, N., Qayyum, A., Bilal, M., Al-Fuqaha, A., & Qadir, J. (2023). Privacy-preserving artificial intelligence in healthcare: Techniques and applications. Computer in Biology and Medicine, 158(23). Leslie, D. (2020). Understanding arti cial intelligence ethics and safety: A guide for the responsible design and implementation of Al systems in the public sector. The Alan Turning Institute. Loh, P.-R., Genovese, G., & McCarrol, S. A. (2020). Monogenic and polygenic inheritance become instruments for clonal selection. Nature, 584, 136-141. Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019). Resistance to Medical Artificial Intelligence. Journal of Consumer Research, 46(4), 1-49. Memon, M. A., Ting, H., Cheah, J.-H., Thurasamy, R., Chuah, F., & Cham, T. H. (2020). Sample Size for Survey Research: Review and Recommendations. Journal of Applied Structural Equation Modeling, 4(2), 1-15. Mesko, B. (2019). The role of artificial intelligence in precision medicine. Expert Review of Precision Medicine and Drug Development, 2(5), 239-241. Mittelstadt, B., Russell, C., & Wachter, S. (2019). Explaining Explanations in AI. In Proceedings of the Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery. Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2020). From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices. Science and Engineering Ethics, 26(4). Mourby, M., Mackey, E., Elliot, M. J., Gowans, H., Wallace, S. E., Bell, J.,... Kaye, J. (2019). Are 'pseudonymised data always personal data? Implications of the GDPR for administrative data research in the UK. Computer Law & Security Review, 34(2), 223-233. Naili, Y. T., Mangkunegara, L. S., Purwono, I., & Baballe, M. A. (2025). Regulatory challenges in ai-based diagnostics: Legal implications of ai use in medical diagnostics. BIO Web of Conference, 152(20). NDPC. (2023). Nigerian Data Protection Act 2023. NDPC. Nwaokocha, B. C. (2025). AI Governance: Foundational For Nigeria's Artificial Intelligence Future. SSRN, 2(1), 1-11. Obermeyer, Z., Powers, B. W., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. Odinakachukwu, E., Okeke, B., Agbonghae, B. E., & Green, A. U. (2024). Artificial Intelligence and Security of the People in Nigeria. Human Rights Law Review, 1(2), 158-167. Okwukwu, M., Olofin, D., & Taiwo, A. A. (2025). Artificial Intelligence in Nigeria Healthcare: A Review of State, Challenges and Opportunities. EthAlca, 4(210), 1-8. Price, N. W., Gerke, S., & Cohen, G. I. (2024). Liability for use of artificial intelligence in medicine. In B. Solaiman, & G. I. Cohen, Research Handbook on Health, Al and the Law (pp. 150-166). Edward Elgar Publishing. Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine Learning in Medicine. The New England Medical Review and Journal, 380(14), 1347-1358. Rajkomar, A., Hardt, M., Howell, M. D., Corrado, G., & Chin, M. H. (2018). Ensuring Fairness in Machine Learning to Advance Health Equity. Annals of Internal Medicine, 169(12). Rimkute, D. (2023). AI and Liability in Medicine: The Case of Assistive-Diagnostic AI. Baltic Journal of Law & Politics,
