ArticleMedical archives (Sarajevo, Bosnia and Herzegovina)2026
Clinical Artificial Intelligence (AI) Liability in the MENA Region: a Comparative Analysis of Hospital, Physician, Suppliers, and Vendor Responsibility.
Article in Medical archives (Sarajevo, Bosnia and Herzegovina), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- From principles to binding rules: A one health governance readiness analysis of Saudi Arabia's AI health policy against global regulatory benchmarks.One health (Amsterdam, Netherlands) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Clinical artificial intelligence (Clinical AI) is now a commonplace tool being used for administrative, diagnostic and therapeutic purposes. However, it gives rise to novel and complex liability questions that will involve multiple actors including physicians, hospitals, suppliers, vendors and more. Objective: The paper compares the relevant laws in nine jurisdictions in the MENA region - including the United Arab Emirates (UAE) and the other GCC states alongside Algeria, Egypt, and Morocco - to understand how evolving legislation, regulation and ethical codes impact fault and damages. Methods: The Clinical AI ecosystem roles are defined, and core legal duties related to consent, data protection, patient rights and safety oversight mapped. The text critiqued (i) the hospital's selection, governance, training, and ongoing post-deployment monitoring responsibilities; (ii) the accountability of clinicians in mixed "decision-support" workflows, including disclosure of the use of AI to patients and standard-of-care implications of accepting or override AI output; and (iii) supplier/vendor exposure through product liability, performance warranties, cybersecurity obligations, licensing/clearance and ongoing post-market updates and supervision. Results: The comparative section shows significant differences between MENA liability models. It also highlights the need for facility-wide incident response proper process that encompasses model drift, cyber events and data-quality failures, which more often cause the harm than algorithm malfunction. Conclusion: In conclusion, improved risk allocation through governance, documentation, contracting and incident reporting, can aid in reducing uncertainty, enhancing patient safety, and facilitating more scalable cross-border deployment of Clinical AI in the region.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.