Mensah, George BennehMijwil, Maad M.Abotaleb, MostafaAli, GumaDutta, Pushan Kumar2025-12-162025-12-162025-01-10Mensah, G. B., Mijwil, M. M., Abotaleb, M., Ali, G., & Dutta, P. K. (2025). High performance medicine: Involving artificial intelligence models in enhancing medical laws and medical negligence matters A Case study of Act, 2009 (Act 792) in Ghana. SHIFAA, 2025, 1-6.3078-2686https://dir.muni.ac.ug/handle/20.500.12260/820This paper explores how explainable artificial intelligence can improve healthcare safety by reducing medical negligence through better training, accountability, and regulation. It identifies gaps in existing health laws, especially in staff capacity-building, patient safety systems, and the responsible use of emerging technologies. The study proposes legal and institutional reforms to strengthen the quality of care, transparency, and risk management in health services. The paper supports Sustainable Development Goals SDG 3, good health and well-being, SDG 4, quality education, SDG 9, industry innovation and infrastructure, and SDG 16, peace, justice, and strong institutions. It also aligns with the aspirations of Uganda's National Development Plan IV for human capital development, digital transformation, improved social services, and strengthened governance.This paper examines Ghana's Interpretation Act, 2009 for applicability in AI medical negligence cases. Doctrinal analysis focuses on causation and liability apportionment provisions. Findings reveal opacity and distributed responsibility issues in attributing algorithm harm via "but-for" and related tests. However, contributory liability and proportionality stipulations provide means for an equitable remedy. Recommendations include codifying AI accountability through updated laws and jurisprudence, plus transparency requirements for medical AI approvals. Ensuring current law dynamically governs emerging technologies remains vital for public welfare. The analysis aims to spur policy adaptations, balancing innovation with adequate causation tests and flexible liability rules for AI medical harms.enAlgorithmic transparencyMedical AILiability apportionmentStatutory interpretationGhana lawArticle