Algorithmic Error, Human Cost: Who Bears Liability for AI-Assisted Diagnostic Systems?

¶1Consider a case that drew widespread attention in American medical circles last year. A radiologist clears a patient based on an "all clear" report generated by the AI system integrated into the imaging platform. A few months later, the patient is diagnosed with advanced-stage cancer. A lawsuit is filed — but the court struggles to identify who to hold accountable. Was the radiologist negligent? Is the software company responsible? Or does liability fall on the hospital that purchased and deployed the system? The case is still ongoing.

¶2This is no longer an isolated incident. AI-assisted diagnostic systems have made their way into clinical practice across radiology, cardiology, oncology, and neurology. Some studies suggest these systems can match — and occasionally surpass — specialist physicians in specific imaging analyses. A 2024 study found that an AI system identified skin cancer more accurately than 58 dermatologists. According to the American Medical Association, nearly three in five American physicians now use AI tools in their routine practice.

¶3Yet the law has fallen far behind this transformation. 2024 data shows a fourteen percent increase in malpractice claims involving AI tools compared to 2022, with the vast majority originating from diagnostic AI systems in radiology, cardiology, and oncology. Despite this, no landmark case has yet resolved an AI-caused medical error in any meaningful legal sense. The legal system can only measure the damage in hindsight — and that delay is a serious problem.

¶4Why Is This So Difficult?

¶5The logic of traditional malpractice law is straightforward: a physician's conduct is compared against the standard of care in their field, and if a deviation is found, liability follows. This mechanism was built to hold humans accountable. When AI enters the picture, it breaks down at multiple points.

¶6The technical dimension of the problem is the "black box" issue. Deep learning algorithms process vast amounts of data to arrive at an output, but neither the developer nor the physician using the system can fully explain why that particular conclusion was reached. Which pixel density or pattern the algorithm weighted most heavily is often unknowable. This makes establishing the causal link — an essential requirement for any liability claim — extraordinarily difficult.

¶7There is also a striking paradox. According to Brown University research, when radiologists miss an abnormality that the AI correctly identified, juries evaluate them far more harshly than colleagues who made the same mistake without using AI. In other words, a physician who uses the system faces both greater operational pressure and heightened legal exposure when something goes wrong. The technology does not make the physician safer — it makes them more vulnerable.

¶8The Liability Triangle

¶9In AI-related medical errors, liability flows through three distinct channels — and none of them fully excludes the others.

¶10Physician liability remains at the heart of the debate. The Federation of State Medical Boards stated clearly in 2024 that physicians remain responsible for the accuracy of their diagnostic decisions regardless of whether they use an AI tool, and that they carry the obligation to critically evaluate algorithmic outputs. Under this framework, no matter how capable the AI becomes, it cannot override the physician's clinical judgment — and when something goes wrong, the physician is the first to answer. Yet the reverse scenario is already conceivable: as AI becomes deeply embedded in certain specialties, a physician who chose not to consult the system may one day be asked in court why they didn't. The question "why did you trust it?" may eventually be replaced by "why didn't you use it?"

¶11Software developer liability is increasingly being pursued through product liability law, on grounds of defective design, inadequate warnings, biased training data, or the underrepresentation of certain patient populations in the algorithm. This area remains unsettled due to a lack of precedent, but it is developing rapidly.

¶12Institutional liability applies to the hospitals and healthcare organizations that purchase and deploy these systems. Which system they chose, how they trained their staff, and whether they established internal oversight mechanisms capable of detecting algorithmic errors — all of these become questions directed at the institution in any litigation.

¶13Where Does the World Stand?

¶14The European Union has taken the most concrete steps. The EU AI Act, which entered into force in August 2024, classifies AI applications in healthcare — including diagnostic systems — as "high-risk." This designation brings with it obligations for technical documentation, mandatory human oversight, transparency, and data governance; full compliance for medical devices will be phased in through 2027. The revised Product Liability Directive has also lowered the bar for plaintiffs: psychological harm and data loss are now compensable, the burden of proof has been eased, and manufacturers can no longer escape liability for harms preventable through software updates. The Council of Europe's Framework Convention, opened for signature in September 2024, further obligates signatories to ensure that AI systems are developed with respect for human rights throughout their entire lifecycle.

¶15The United States is taking a different path. The FDA has approved over eight hundred AI-enabled medical devices, but there is still no federal law specific to AI in healthcare. The American Law Institute's May 2024 restatement of medical malpractice law shifts the standard of care toward a patient-centered reasonable care framework — a change that will significantly shape how courts interpret the "reasonable physician" standard in AI-integrated clinical settings. The variation between states, however, creates substantial unpredictability: the same type of case can produce entirely different outcomes depending on jurisdiction.

¶16Japan has chosen to operate through ethical guidelines and professional standards. The Japanese Society for Artificial Intelligence framework emphasizes accountability and transparency, but carries no binding force. It offers short-term flexibility while carrying the long-term risk of a standards vacuum.

¶17Where Does Turkey Stand?

¶18Turkey has no AI-specific healthcare law. The 2021–2025 National Artificial Intelligence Strategy provides a general framework but does not define concrete obligations. In practice, AI systems in healthcare are evaluated indirectly under the Personal Data Protection Law, the Medical Deontology Regulation, the Patient Rights Regulation, and the Basic Law on Health Services. None of these instruments directly addresses autonomous decision-making mechanisms or the legal liability arising from algorithmic error.

¶19Academic debate continues around four approaches to AI's legal status: the property approach, which treats AI as a programmed product; the slavery analogy, which foregrounds the relationship of control and oversight; the legal personhood approach, which argues for granting AI at least limited standing; and the electronic personhood approach, which proposes a more pragmatic, constrained status. Each implies a different distribution of liability — but none has found a foothold in positive law.

¶20Recent developments suggest that change, however slow, has begun. A bill submitted to the Grand National Assembly in July 2025 aims to establish a legal definition of AI and grant the Information and Communication Technologies Authority significant administrative enforcement powers. The Istanbul 14th Commercial Court of First Instance's ruling of May 2025 marked the first time a Turkish court explicitly incorporated the use of an AI-based tool into its legal reasoning — a small but meaningful inflection point. The EU's reach also cannot be ignored: a Turkish manufacturer whose product is used in the EU market is subject to the full requirements of the EU AI Act. This compels Turkish health technology companies to fundamentally restructure their compliance frameworks without delay.

¶21A Final Word

¶22The legal landscape surrounding AI in healthcare sends a clear message: the technology has moved fast and left difficult questions in its wake. Who bears the loss, how the burden of proof is distributed, how far a physician's reliance on an algorithm can serve as a defense — all of these remain contested. And this uncertainty is not merely theoretical; for patients who have suffered serious harm due to a misdiagnosis, the consequences are entirely concrete.

¶23Turkey may be late to this conversation, but the window has not closed. Drawing on the experience of other legal systems, it remains possible to build a framework that neither stifles innovation nor leaves victims outside the reach of the law. Achieving that will require law, technology, and medicine to think together — and to start doing so now.

This article has been prepared within the scope of our firm's AI law and health law practice areas. For legal advice on malpractice, compliance, or contract law issues arising from the use of AI systems, please do not hesitate to contact us.