Artificial Intelligence (AI) and Machine Learning in Biochemical research and clinical diagnostics regarding opportunities, challenges and prospects in Nigeria

Healthcare services in Nigeria continue to face problems such as late diagnosis, limited laboratory staff, and dependence on manual laboratory methods. Meanwhile, Artificial Intelligence and Machine Learning are changing how biomedical data is processed worldwide. This narrative review examines how AI and ML are used in biochemical research and clinical diagnostics, using Nigeria as the main focus. Literature published between 2019 and 2025 was retrieved from PubMed, Scopus, Web of Science and Google Scholar. The review shows that AI can enhance biomarker discovery, protein structure prediction, drug development, genomics, and automation of laboratory processes. In Nigeria, however, uptake is slow due to weak digital infrastructure, scattered health data, inadequate training, and absence of locally validated AI models. Most current AI tools were trained with data from outside Africa, raising questions about their accuracy for Nigerian populations. The paper concludes that Nigeria needs investment in digital systems, human capacity, data policy, regulatory frameworks, and local research to benefit from AI. Five practical recommendations are provided for policy makers and health institutions.