Artificial Intelligence Systems and Medical Negligence: An Overview and Perspective of a Case Study in Ghana Civil Procedure Rules, 2004 (C.I. 47)




Medical Negligence, Artificial Intelligence, Machine learning, Evidence, Law


Objective: This article discusses the evidentiary requirements for demonstrating scientific negligence under Ghana’s Civil Procedure Rules 2004 (C.I. 47) in the context of emerging artificial intelligence (AI) diagnostic and treatment structures.
Method: Legal analysis examines gaps in satisfying burden of proof and standards of evidence, obstacles that restrict evidence collection on AI device deficiencies, and suggestions for adapting legal responsibility policies to AI’s technical opacity.
Findings: The present inability to interrogate algorithms, limited access to proprietary training data and methods, lack of diagnosed standards of care for software-based decision-makers, and shortage of qualified professional witnesses pose massive evidentiary challenges for plaintiffs seeking to confirm AI negligence.
Conclusions/Recommendations: Standards strengthening algorithmic transparency, auditability, and explainability could ease evidentiary burdens for affected patients. Strict liability schemes and IP protections balancing public safety and innovation aims need to be considered moving forward.
Scientific Contributions: This work adapts traditional medical liability systems to today’s realities of increasing reliance on AI in health care and proposes several improvements.


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