Ethical, Legal, and Professional Vulnerabilities: Healthcare Providers’ Perceptions of Liability in AI-Assisted Surgical and Diagnostic Protocols

Abstract

Background: The rise of clinical algorithmic tools has transformed healthcare delivery, establishing artificial intelligence protocols as pervasive standards in diagnostic and surgical workflows. Despite their diagnostic precision and operational efficiency, empirical investigation into the professional, legal, and epistemic vulnerabilities experienced by frontline clinicians under prevailing liability doctrines remains limited.

Objective: This study developed and tested the Integrated Vulnerability–Liability–Adoption Model to examine how epistemic vulnerability, civil liability exposure, and criminal culpability fears influence clinical trust deficits, defensive medical practices, and protocol adoption intentions across diagnostic and surgical specialists.

Methodology: A cross-sectional quantitative survey design was conducted with licensed specialists (N = 284) across tertiary hospitals using the researcher-developed Healthcare Providers’ Liability Perceptions Scale. Data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) in SmartPLS 4, evaluating measurement parameters, structural path coefficients via 5,000 bootstrap resamples, mediation effects, and multi-group differences between diagnostic and surgical cohorts.

Result: Epistemic vulnerability exerted strong positive effects on perceived civil liability (beta = 0.462, p < 0.001) and criminal culpability (beta = 0.398, p < 0.001). Perceived civil liability significantly inhibited protocol adoption (beta = -0.318, p < 0.001) and drove widespread defensive utilisation behaviours (beta = 0.512, p < 0.001). Clinical trust deficit partially mediated the relationship between liability perception and adoption resistance (beta = -0.112, p < 0.001). Institutional resilience negatively moderated defensive posturing (beta = -0.184, p = 0.001). Multi-group analysis revealed that liability-induced adoption resistance and defensive behaviours were significantly more pronounced among surgical specialists than diagnostic clinicians.

Conclusion: Algorithmic opacity and disproportionate legal exposure undermine physician trust, transforming clinicians into institutional liability shields and inducing defensive clinical redundancies. Sustainable technological integration requires statutory liability reform, institutional safe harbours, and transparent override frameworks that equitably distribute risk across developers, healthcare organisations, and practitioners.