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

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.

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Publication Details

Journal
Shifaa.
Published
2025-01-10
DOI
https://doi.org/10.70470/shifaa/2025/001
Citations
8
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
5.23
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article

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

Guma Ali, Pushan Kumar Dutta, Maad M. Mıjwıl, Mostafa Abotaleb et al.
8 citations
Shifaa.
Artificial Intelligence in Healthcare and Education
5.23
article

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

Guma Ali, Pushan Kumar Dutta, Maad M. Mıjwıl, Mostafa Abotaleb, George Mensah
article en
8 citations

Abstract

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.

Shifaa.Vol. 2025
South Ural State University (RU), Africa Center (US), Iraqi University (IQ), Muni University (UG)
Openalex Percentile: Top 5%
Artificial Intelligence in Healthcare and Education
5.23
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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 — Guma Ali, Pushan Kumar Dutta, et al. · Shifaa. (2025) | TGRS Research Map | TGRS