Exploring Explainable Artificial Intelligence (XAI) to Enhance Healthcare Decision Support Systems in Nigeria

Authors

  • Undie Franka Anyama Department of Mechanics and Control Processes, Academy of Engineering, Рeoples’ Friendship University of Russia, RUDN University, 6 Miklukho, Maklaya St, Moscow, 117198, Russian Federation https://orcid.org/0009-0006-3066-6761
  • Kruglova Larisa Vladimirovna Department of Mechanics and Control Processes, Academy of Engineering, Рeoples’ Friendship University of Russia, RUDN University, 6 Miklukho, Maklaya St, Moscow, 117198, Russian Federation https://orcid.org/0000-0002-8824-1241
  • Okache Matthew Okache Federal polytechnic Nekede, Owerri. P M B 1036, Owerri Imo State, Nigeria
  • Undie Victor Agorye University of Calabar, Calabar
  • Aloye Racheal Aniah Management in technical systems, Academy of Engineering, People’s Friendship University of Russia. RUDN University 6 Miklukho Maklaya St, Moscow, 117198, Russian Federation

DOI:

https://doi.org/10.54536/jir.v2i3.3450

Keywords:

Explainable Artificial Intelligence, Healthcare Decision Support Systems, Nigeria, XAI

Abstract

In Nigeria, the healthcare sector faces big challenges. Limited access to quality services and not enough resources are major issues. Using Artificial Intelligence (AI) could help improve healthcare. But understanding AI predictions is hard, especially in healthcare where transparency is crucial. This article looks at Explainable AI (XAI) to help with this problem in Nigeria. It talks about XAI techniques like feature importance examination, model-agnostic methods (e.g., LIME, SHAP), and interactive visualization tools. These tools can make AI models easier to understand and help with decision-making. A literature review was done to see how XAI can help healthcare in Nigeria. The review included scholarly articles, books, and reports on AI in Nigerian healthcare. We looked at methods from past XAI studies to find common approaches and best practices. XAI offers techniques that make AI models easier to understand in healthcare systems. These techniques include feature importance examination, model-agnostic methods, and interactive visualization tools. Case studies from Nigeria show how XAI is used in areas like disease diagnosis, treatment recommendations, and public health interventions. The findings show the importance of XAI in solving interpretability issues in healthcare AI, especially in places with limited resources like Nigeria. By explaining why AI makes certain predictions, XAI helps healthcare workers make better decisions for Nigerian patients. However, more research is needed to improve XAI techniques for Nigeria’s healthcare system. Policymakers and healthcare leaders should focus on using XAI-enabled systems to drive innovation and improve healthcare outcomes in Nigeria.

References

Abubakar, I., et al. (2022). The Lancet Nigeria commission: Investing in health and the future of the nation. Lancet Commissions, 399(10330), 1155–1200. https://doi.org/10.1016/S0140-6736(21)02488-0

Adenubi, A., Oduroye, A., & Akanni, A. (2024). Artificial intelligence (AI) in healthcare: Transforming diagnosis and treatment. ResearchGate.

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 Making, 20, 310. https://doi.org/10.1186/s12911-020-01332-6

Asan, O., Bayrak, A. E., & Choudhury, A. (2020). Artificial intelligence and human trust in healthcare: Focus on clinicians. Journal of Medical Internet Research, 22(6), e15154. https://doi.org/10.2196/15154

Fasanmade, O. A., & Dagogo-Jack, S. (2015). Diabetes care in Nigeria. Annals of Global Health, 81(6), 821–829. https://doi.org/10.1016/j.aogh.2015.12.012

Hassija, V., Chamola, V., & Mahapatra, A. (2024). Interpreting black-box models: A review on explainable artificial intelligence. Cognitive Computation, 16(1), 45–74. https://doi.org/10.1007/s12559-023-10179-8

Han, H., & Liu, X. (2021). The challenges of explainable AI in biomedical data science. BMC Bioinformatics, 22(Suppl 12), 443. https://doi.org/10.1186/s12859-021-04368-1

Iloh, G. U., Ofoedu, J. N., Njoku, P. U., Odu, F. U., & Ifedigbo, C. V. (2020). Challenges of the Nigerian healthcare system in the 21st century. Annals of Medical and Health Sciences Research, 10(3), 487–492.

Olumade, T. J., Adesanya, O. A., Fred-Akintunwa, I. J., Babalola, D. O., Oguzie, J. U., Ogunsanya, O. A., George, U. E., & Akin-Ajani, O. D. (2020). Infectious disease outbreak preparedness and response in Nigeria: History, limitations, and recommendations for global health policy and practice. AIMS Public Health, 7(4), 736–757. https://doi.org/10.3934/publichealth.2020057

Payrovnaziri, S. N., Chen, Z., Rengifo-Moreno, P., Miller, T., Bian, J., Chen, J. H., Liu, X., & He, Z. (2020). Explainable artificial intelligence models using real-world electronic health record data: A systematic scoping review. Journal of the American Medical Informatics Association, 27(7), 1173–1185. https://doi.org/10.1093/jamia/ocaa053

Praveen, S., & Joshi, K. (2023). Explainable artificial intelligence in healthcare: How XAI improves user trust in high-risk decisions. In A. E. Hassanien, D. Gupta, A. K. Singh, & A. Garg (Eds.), Explainable edge AI: A futuristic computing perspective (pp. 112–130). Springer. https://doi.org/10.1007/978-3-031-18292-1_6

Qiu, J., et al. (2023). Large AI models in health informatics: Applications, challenges, and the future. IEEE Journal of Biomedical and Health Informatics, 27(12), 6074–6087. https://doi.org/10.1109/JBHI.2023.3316750

Reddy, S., Fox, J., & Purohit, M. P. (2019). Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine, 112(1), 22–28. https://doi.org/10.1177/0141076818815510

Rodríguez-Pérez, R., & Bajorath, J. (2020). Interpretation of machine learning models using Shapley values: Application to compound potency and multi-target activity predictions. Journal of Computer-Aided Molecular Design, 34, 1013–1026. https://doi.org/10.1007/s10822-020-00314-0

Saraswat, D., et al. (2022). Explainable AI for healthcare 5.0: Opportunities and challenges. IEEE Access, 10, 84486–84517. https://doi.org/10.1109/ACCESS.2022.3197671

Sheu, R. K., & Pardeshi, M. S. (2022). A survey on medical explainable AI (XAI): Recent progress, explainability approach, human interaction, and scoring system. Sensors, 22(20), 8068. https://doi.org/10.3390/s22208068

Tiwari, R. (2023). Explainable AI (XAI) and its applications in building trust and understanding in AI decision-making. International Journal of Science Research Engineering Management, 7(01). https://doi.org/10.55041/ijsrem17592

UN Department of Economic and Social Affairs. (2019). UN world population prospects. https://population.un.org/wpp/

Welcome, M. O. (2011). The Nigerian healthcare system: Need for integrating adequate medical intelligence and surveillance systems. Journal of Pharmacy and Bioallied Sciences, 3(4), 470–478. https://doi.org/10.4103/0975-7406.90100

Yelne, S., Chaudhary, M., Dod, K., Sayyad, A., & Sharma, R. (2023). Harnessing the power of AI: A comprehensive review of its impact and challenges in nursing science and healthcare. Cureus, 15(11), e49252. https://doi.org/10.7759/cureus.49252

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Published

2024-09-27

How to Cite

Undie, F. A., Kruglova, L. V., Okache, M. O., Undie , V. A., & Aloye, R. A. (2024). Exploring Explainable Artificial Intelligence (XAI) to Enhance Healthcare Decision Support Systems in Nigeria. Journal of Innovative Research, 2(3), 41–48. https://doi.org/10.54536/jir.v2i3.3450

Issue

Vol. 2 No. 3 (2024): Journal of Innovative Research

Section

Research Articles

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Copyright (c) 2024 Undie Franka Anyama, Kruglova Larisa Vladimirovna, Okache Matthew Okache, Undie Victor Agorye, Aloye Racheal Aniah

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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