Aug 2026· International Journal of Law Management & Humanities· Vol 9, pp. 2321-2333· 0 citations· 3 references
TL;DR
Analysis of six areas of artificial intelligence in healthcare concludes that a workable framework must combine the principles of medical negligence law with data protection, consumer protection and sectoral safety requirements, together with governance mechanisms for artificial intelligence such as human oversight, audit logs, explainability and grievance redressal.
Abstract
Artificial intelligence is beginning to play an important role in contemporary healthcare, where it is applied to diagnosis, clinical support, triage, imaging, remote monitoring, drug development and hospital management. A problem of liability arises because artificial intelligence produces outputs that can contribute to patient risk, yet the system itself cannot bear liability in the way a medical professional, a hospital or a manufacturer does. A review of the literature shows that no dedicated regime governs artificial intelligence liability in healthcare, and it brings out the associated questions of justice, bias, accountability, transparency and information security. This article analyses six areas: patient data confidentiality, patient consent, algorithmic bias and discrimination, misdiagnosis, accountability, and the reuse of previous patient records. It concludes that a workable framework must combine the principles of medical negligence law with data protection, consumer protection and sectoral safety requirements, together with governance mechanisms for artificial intelligence such as human oversight, audit logs, explainability and grievance redressal. The approach is qualitative and comparative, resting on a study of the literature and a content analysis of existing legal instruments, including the Digital Personal Data Protection Act, 2023, the Ayushman Bharat Digital Mission privacy framework, and the guidance of the World Health Organization on artificial intelligence in health. The findings indicate that the existing legal framework offers a partial answer to these questions but not an integrated one. Healthcare artificial intelligence should be treated as a high-risk setting governed by layered liability: the developer answers for design defects and bias, the deploying entity, ordinarily the hospital, answers for implementation and monitoring, and the medical professional answers for negligence where the use of artificial intelligence still involves human judgment.
It is concluded that the responsible use of artificial intelligence in medicine depends on human supervision, professional training, and the integration of bioethical principles, ensuring technological innovation aligned with safety and patient-centered care.
Hélio Silva Dias, Hengrid Graciely Nascimento Silva, Italo Bezerra et al.· Revista de Estudos Interdisc...· 0 citations
This review addresses the primary ethical principles relevant to AI in medicine - including respect for patient autonomy, beneficence, non-maleficence, and justice - alongside key legal frameworks with respect to liability, data protection, regulatory compliance, and algorithmic transparency.
Sebastian Schleidgen, O. Friedrich· European journal of internal...· 0 citations
Following the withdrawal of the proposed AI Liability Directive in 2025, claims concerning harm caused by healthcare AI continue to be addressed through product liability, national medical liability and sectoral regulation. This article analyses that architecture through three deployment archetypes, a CE-marked radiology triage tool, a machine-learning deterioration score and an ambient clinical scribe, across Ireland, Italy and Germany. It argues that five recurrent gaps persist notwithstanding the revised Product Liability Directive and the AI Act: evidence asymmetry, documentation integrity, multi-actor control, warning and reliance, and causation under uncertainty. The revised Product Liability Directive improves software coverage and producer-side proof tools, while the AI Act, together with the Medical Devices Regulation where applicable, strengthens the evidential environment through logging, documentation, oversight and post-market duties. Yet those gains are uneven in time and scope and do not eliminate the claimant-facing difficulty created by vendor-held artefacts, distributed control and negligence-side causal proof. The most immediate improvements therefore lie in upstream governance: procurement clauses on artefact retention and exportability, express contractual control-point allocation, and deployment-stage assessment of whether system design is genuinely consistent with the human verification expected of clinical users.
O. Tujjar, Gabriele Ientile, Francesca Toppetti· European Journal of Risk Reg...· 0 citations
The healthcare sector is one of the most important fields that has witnessed tremendous developments in benefiting from digital transformation applications, particularly through the use of intelligent robots in surgical procedures or post-medical care, as well as software systems that assist in disease diagnosis and treatment recommendation. It has therefore become necessary to examine the extent to which traditional civil liability rules are capable of accommodating the transformations imposed by the use of artificial intelligence in the medical field. This is done through analyzing the conditions for establishing liability, identifying its parties, and highlighting the main challenges raised at both the practical and judicial levels. Legal scholarship has given considerable attention to the issue of civil liability, especially concerning professionals in their relationships with third parties. The essence of this liability lies in the obligation to compensate damage caused to others as a result of the fault of these professionals whenever the elements of liability are met, namely fault, damage, and causation. This traditional concept of liability has been sufficient to resolve medical liability issues, based on the physician’s personal act, negligence, or lack of prudence, whenever it results in misdiagnosis, inappropriate treatment, or surgical intervention without compliance with technical standards. However, digital transformation and technological development in the world, particularly in the healthcare sector, have led to the use of artificial intelligence as a technical tool that facilitates medical decision-making. It processes data and responds rapidly and continuously in order to achieve better treatment outcomes. Nevertheless, despite the multiple advantages of adopting artificial intelligence in the medical field, the complexity and intertwinement of relationships make the allocation of liability in cases of patient harm marked by ambiguity and uncertainty, in the absence of clear legal legislation that keeps pace with contemporary developments.Problematic: To what extent are the general rules of civil liability adequate for determining liability for medical errors resulting from the use of artificial intelligence?Importance and Objectives of the Study: The importance of this topic lies in its contemporary nature on the one hand, and in shedding light on the legal nature of artificial intelligence systems on the other. It also addresses the elements of civil liability arising from the use of artificial intelligence in the medical field, in an attempt to create a balance between technological development and legal safeguards for the protection of patients’ rights.Methodology: To address this topic and answer its main problematic, the study adopts an inductive approach based on reading and analyzing legal texts, with a particular focus on Moroccan law. It also relies on a descriptive-analytical method by describing and analyzing different hypotheses regarding the use of artificial intelligence technologies and applications in the medical field and their impact on the distribution of liability between companies producing AI systems, physicians using these technologies, and hospitals providing such technologies..
Sabouny Abdelhaq, Sidi Mohamed, Ibn Abdelah et al.· PAIN, JOINTS, SPINE· 0 citations
AI systems play a more significant role in decision-making in healthcare, transport, finance, and commerce. Their autonomous or adaptive operations can inflict bodily injury, cause economic loss, violate privacy, and damage reputation, while obscuring fault, causation, and the identity of the responsible actor. This paper assesses whether the conventional civil-liability rules provide adequate compensation in situations where harm results from the conduct of the AI provider, producer, programmer, deployer, operator, data supplier. Through a comparative analytic method, the study looks at fault-based doctrines, presumed doctrines, strict doctrines and product liability doctrines of the civil law system. It studies Regulation (EU) 2024/1689 on artificial intelligence, Directive (EU) 2024/2853 on liability for defective products and Article 82 of Regulation (EU) 2016/679 on compensation for unlawful personal-data processing. Next it assesses judicial damages, corrective remedies, mandatory insurance, compensation funds, joint liability and presumptions of proof. The study finds fault-based liability remains useful where negligent design, testing, deployment, supervision or use can be proved but may fail when technical opacity blocks access to evidence. As such, effective victim protection should rely on a layered model, it would combine, ordinary civil liability encompassing fault-based damage claims and risk-based duties, disclosure and record-preservation obligations, rebuttable presumptions, compulsory insurance for defined high-risk systems, and the residual compensation fund. This model enhances access to full and timely reparation while preserving legal certainty.
Jyan Bahil Jadaan, Dr. Qayssar Abbas Hasan· Indonesian Journal of Law an...· 0 citations
This study aims to examine the construction of criminal liability in cases of medical malpractice involving artificial intelligence and to identify the normative gaps within contemporary health law that hinder effective accountability. It further seeks to formulate a responsive legal framework capable of addressing the challenges posed by the integration of advanced technologies in medical practice. The research employs normative legal methodology using statute and conceptual approaches. The statute approach analyzes existing legal provisions governing healthcare and criminal liability, while the conceptual approach explores doctrinal principles such as fault, negligence, and responsibility in technologically mediated environments. Data are analyzed using a descriptive prescriptive method to both explain current legal conditions and propose normative solutions. The findings reveal that existing legal frameworks remain anthropocentric and are unable to adequately address the distributed nature of responsibility in AI mediated healthcare. The absence of explicit regulations on the use of artificial intelligence creates a normative vacuum, leading to uncertainty in attributing criminal liability among physicians, developers, and healthcare institutions. The study also finds that traditional doctrines such as mens rea and actus reus are increasingly difficult to apply in cases where decision making involves algorithmic systems. As a result, there is a need to reconceptualize criminal responsibility through more adaptive models, including shared responsibility and selective strict liability. This research concludes that legal reform is essential to ensure accountability, legal certainty, and patient protection in the era of digital healthcare. It proposes the development of integrated and forward looking legal frameworks that align technological innovation with fundamental principles of criminal law.
Nirwan Afandy, Hasbuddin Khalid, Satri Hasyim· Golden Ratio of Law and Soci...· 0 citations