AI in Medicine: Who’s Liable When Technology Goes Wrong?
In most civil legal disputes, the core issue often revolves around who should bear the cost of misconduct — with both parties typically claiming innocence. As artificial intelligence (AI) becomes more deeply integrated into healthcare, a critical question has surfaced: if a doctor relies on AI tools for diagnosis and treatment, and a mistake occurs harming the patient, who should be held responsible?
This article examines the growing legal challenges posed by AI’s involvement in clinical decision-making and treatment recommendations.
The Growing Role of AI in Healthcare
Artificial intelligence, especially machine learning (ML) and deep learning (DL) systems, is increasingly being deployed across hospitals and healthcare settings worldwide. From early stroke detection and diabetic retinopathy screening to forecasting hospital admissions, AI has been transforming how healthcare is delivered.¹
Multiple studies and surveys highlight how AI has enhanced operational efficiency, enabling faster, more accurate diagnostic processes and better tracking of treatment outcomes. In oncology, radiologists now harness AI algorithms to detect subtle patterns in imaging data — including CT, MRI, and PET scans — that often elude the human eye.² ³
For example, AI-based tools have shown promise in the early detection of lung, prostate, and breast cancers. Algorithms can now analyze 2D and 3D mammography images to flag abnormalities, aiding radiologists in achieving greater diagnostic precision.⁴ ⁵ However, despite its potential, many AI systems currently on the market are limited by insufficient clinical validation data, resulting in inconsistent performance outcomes.⁶
AI is also being used for automated tumor characterization, enabling more personalized treatment planning and monitoring of disease progression.
Notable AI-driven healthcare platforms like IBM Watson Health, Google DeepMind Health, Eyenuk, IBEX Medical Analytics, Aidoc, and Butterfly iQ have gained traction among physicians, radiologists, and healthcare providers for diagnosis and treatment management.
Accountability in the Age of AI-Driven Healthcare
When AI makes a clinical error that leads to patient harm, determining liability becomes complex. Physicians might argue that any shortcomings stem from the technology itself, while developers and manufacturers could claim that final medical decisions rest in the hands of doctors.
This ongoing debate highlights a significant gap in medical accountability frameworks. Presently, no universally established guidelines clarify the division of liability among AI developers, healthcare providers, and regulatory bodies in cases of AI-generated clinical errors.
To address this issue, comprehensive legal frameworks are needed to clearly define responsibilities and protect patient welfare. Additionally, policies should establish where legal liability lies along the AI supply chain.
Legal and Ethical Challenges in Medical AI
While AI’s contributions to medical diagnosis and treatment are undeniable, they come paired with considerable legal and ethical concerns — especially regarding data security, accountability, and compliance with existing regulations.⁷
AI tools rely heavily on sensitive patient health records, raising questions about data protection, consent, and transparency. To safeguard personal health data, regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. were introduced in 1996.⁸
Another pressing issue is algorithmic bias. AI systems trained on unbalanced datasets can inadvertently produce discriminatory recommendations, affecting clinical decision-making for certain patient groups.
Moreover, the lack of transparency in many AI tools — often referred to as “black box” models — makes it difficult to understand how specific decisions are made. This ambiguity hinders accountability, and both developers and healthcare professionals need to address these limitations through improved system explainability and rigorous clinical validation.⁹
Physicians, though aware of AI’s benefits, sometimes hesitate to incorporate it into clinical practice for fear of legal liability should an AI-guided decision deviate from standard care protocols.
Current regulatory frameworks, such as those of the U.S. Food and Drug Administration (FDA), primarily focus on traditional medical devices and are ill-suited to govern rapidly evolving AI software. As a result, there’s a growing need for updated, AI-specific regulations that both foster innovation and ensure patient safety.
