Comparative Analysis of AI Liability Regimes in Healthcare Law

Thesis Abstract

This study investigates how different AI liability regimes in healthcare law allocate accountability for errors, omissions, and harms arising from AI-driven medical decision-support, diagnostic, and therapeutic tools, addressing the problem of regulatory fragmentation across jurisdictions and its impact on patient safety, clinician responsibility, and innovation incentives. The aim is to compare statutory, regulatory, and common-law approaches to AI liability in healthcare and to identify which models most effectively balance patient protection with feasible clinical deployment of AI technologies. Specific objectives include (1) mapping the current liability frameworks in at least three representative jurisdictions with advanced healthcare AI adoption, (2) evaluating the adequacy of fault-based, strict-liability, no-fault, and product-liability paradigms in addressing AI-specific risks such as algorithmic bias, opacity, and autonomous decision-making, (3) assessing the role of professional duty of care, informed consent, and hospital/AI vendor accountability in each regime, (4) testing hypotheses regarding the correlation between liability predictability and AI adoption rates in healthcare, and (5) proposing a harmonized analytical framework to guide policymakers and stakeholders. The research employs a comparative mixed-methods design, combining doctrinal analysis with empirical data. The doctrinal component systematically analyzes statutory codes, case law, and regulatory guidance from the United States, the European Union, and the United Kingdom, supplemented by references to Canada for triangulation. The empirical component draws on a dataset of 180 anonymized medical malpractice claims involving AI-assisted interventions, 60 clinician surveys, and 30 regulator interviews conducted between 2022 and 2025. Data collection instruments include a coding framework for liability attributes (fault, causation, foreseeability), survey questionnaires measuring perceived liability risk and decision confidence among clinicians, and semi-structured interview guides for regulators and institutional risk managers. Validity and reliability are enhanced through triangulation, pilot testing of instruments (n=20 clinicians), inter-coder reliability checks (Cohen’s kappa >0.80), and multiple imputation for missing survey data. Analytical techniques comprise quantitative regression analyses to test relationships between liability predictability and AI adoption metrics, event history analysis for malpractice claim timelines, and thematic analysis of interview transcripts to extract regulatory rationales and practical challenges. A cross-jurisdictional conceptual model maps liability regimes against AI lifecycle stages (data governance, model validation, clinical deployment, post-market surveillance, and remediation), with theoretical grounding in liability law theories (fault-based causation, risk regulation theory) and safety ethics (precautionary principle). Expected findings indicate that strict-liability or no-fault regimes paired with robust post-market surveillance and explicit allocation of vendor responsibility yield lower uncertainty for healthcare providers while maintaining patient protection, whereas broad fault-based regimes without clear allocation mechanisms correlate with defensive medicine and reduced AI innovation. The study anticipates identifying best-practice configurations, such as mandatory disclosure of AI systems’ limitations in consent processes, mandatory post-market monitoring obligations on developers, and clarified duty-of-care standards that incorporate algorithmic reliability thresholds. The contribution to knowledge is twofold first, it offers an empirically grounded, comparative typology of AI liability regimes in healthcare that clarifies regulatory incentives and risk distribution; second, it proposes a harmonized analytical framework and policy recommendations capable of guiding cross-border governance and informing future soft-law guidelines, national enactments, and EU-level harmonization efforts. The main conclusion posits that liability systems combining predictable fault allocation with rigorous accountability for developers and transparent risk communication to patients best reconcile patient safety with continuous AI innovation. Recommendations include adopting model clauses for AI vendor liability in healthcare agreements, mandating standardized algorithmic transparency disclosures within consent forms, establishing independent post-market surveillance bodies, and pursuing international cooperation to align core liability principles across jurisdictions.