Bridging legal education and practice: an empirical insights into artificial intelligence accountability in healthcare / Nurus Sakinatul Fikriah Mohd Shith Putera ... [et al.] - Mohd Shith Putera, Nurus Sakinatul Fikriah; Saripan, Hartini; Abu Hassan, Rafizah; Prihandono, Iman (Universiti Teknologi MARA, 2024)
Legal education calls for the assessment of the new dimension of Artificial Intelligence accountability. As AI revolutionises diagnostics, treatment planning, and patient care in an unprecedented way – it forces us to rethink how healthcare is delivered. Nonetheless, complex legal challenges are introduced by this integration, particularly around accountability for AI-influenced decisions that lead to patient injury or harm. Confronting these concerns ensures that legal graduates are armed with the required skills, foresight and resilience to address emerging legal disputes in a technologically driven world. Admittedly, despite extensive theoretical discussions on the challenges of and approaches to AI accountability, there remains a significant gap in their empirical validation and practical implementation in legal settings. Thus, this research aims to enhance legal frameworks for AI accountability in healthcare by empirically testing the effectiveness of existing theories and developing actionable steps, bridging the gap between theoretical discussions and practical legal applications. Adopting a mixed-methods approach, the research incorporates qualitative analysis from document review with quantitative data of perspectives from 62 legal professionals to comprehend aspects of accountability that demand further scrutiny. The findings indicate significant discrepancies between existing legal frameworks and the rapid development of AI technologies – confirming the general consensus among stakeholders on the exigency to reinvent accountability approach. This research also discovered areas that legal professionals perceived as imperative to AI accountability including but not limited to the development of guidelines on the determination of liability based on roles and responsibilities of stakeholders, training and audit protocol for the deployment of AI in healthcare, AI transparency and explainability standards, comprehensive oversight structures and integration standards of AI into medical practice. These recommendations aim to drive the legal environment with the protection of patient rights and responsible development and use of AI in healthcare at the epicentre. Thus, ensuring that technological boons are reaped safely and ethically.