Liability for harm caused by AI in healthcare: an overview of the core legal concepts

Abstract

The integration of artificial intelligence (AI) into healthcare in Africa presents transformative opportunities but also raises profound legal challenges, especially concerning liability. As AI becomes more autonomous, determining who or what is responsible when things go wrong becomes ambiguous. This article aims to review the legal concepts relevant to the issue of liability for harm caused by AI in healthcare. While some suggest attributing legal personhood to AI as a potential solution, the feasibility of this remains controversial. The principal–agent relationship, where the physician is held responsible for AI decisions, risks reducing the adoption of AI tools due to potential liabilities. Similarly, using product law to establish liability is problematic because of the dynamic learning nature of AI, which deviates from static products. This fluidity complicates traditional definitions of product defects and, by extension, where responsibility lies. Exploring alternatives, risk-based determinations of liability, which focus on potential hazards rather than on specific fault assignments, emerges as a potential pathway. However, these, too, present challenges in assigning accountability. Strict liability has been proposed as another avenue. It can simplify the compensation process for victims by focusing on the harm rather than on the fault. Yet, concerns arise over the economic impact on stakeholders, the potential for unjust reputational damage, and the feasibility of a global application. Instead of approaches based on liability, reconciliation holds much promise to facilitate regulatory sandboxes. In conclusion, while the integration of AI systems into healthcare holds vast potential, it necessitates a re-evaluation of our legal frameworks. The central challenge is how to adapt traditional concepts of liability to the novel and unpredictable nature of AI—or to move away from liability towards reconciliation. Future discussions and research must navigate these complex waters and seek solutions that ensure both progress and protection.

Liability of Health Professionals Using Sensors, Telemedicine and Artificial Intelligence for Remote Healthcare

“…On the other hand, autonomous AI systems (or strong AI tending to progressively become autonomous) might “independently identify normal X-rays and generate reports, bypassing radiologists” [ 19 ]. Thus, when AI is autonomous, tension will arise about the responsibility, especially when the physicians struggle to understand how the system works or when they are forced to seek multiple ways of validating their decision to follow or reject AI recommendations [ 32 ].…”

“…Finally, having different responsibilities at different stages of the sensor/AI system’s lifecycle seems to be a functioning approach used in the EU [ 32 ], particularly when there is not one piece of technology but a heterogeneous group with varying liability risks. It was mentioned in the article that there would be new regulation created specifically in the context of sensor/AI systems in healthcare, which could be a good way to provide a better framework, especially when it has been proven that liability laws can “encourage adoption of technologies that reduce harm (to users, workers, or the public)” [ 60 ].…”

Artificial intelligence in healthcare: an Italian perspective on ethical and medico-legal implications

“…However, this advantage could be restricted as many modern systems lack transparent reasoning. Moreover, the main disparity between human and AI decision-making lies in the morality of their actions ( 28 ). Indeed, human decision-making is influenced by moral considerations, a dimension completely absent in computer systems ( 2 ).…”

“…The challenge arises when AI computational power far exceeds human intellect. In such instances, if AI is not embedded into care standards, care providers must take the risk by choosing between adherence to recognized guidelines or relying on AI outputs ( 28 ). The lack of a clear definition of the responsibility of both AI and the physician who uses it further complicates the ability to assess fault-based liability, due to the ambiguity surrounding carelessness ( 28 ).…”

“…In such instances, if AI is not embedded into care standards, care providers must take the risk by choosing between adherence to recognized guidelines or relying on AI outputs ( 28 ). The lack of a clear definition of the responsibility of both AI and the physician who uses it further complicates the ability to assess fault-based liability, due to the ambiguity surrounding carelessness ( 28 ). An alternative to fault-based liability is represented by strict liability (also known as no-fault liability), which simplifies the burden for claimants.…”