If Medical AI Makes a Misdiagnosis, Who is Sued?

Correction (19 October 2026): An earlier version said the European Medicines Agency classifies diagnostic software as a medical device; in the EU such software falls under the Medical Device Regulation and notified bodies, and we have also corrected the description of the European Parliament's position on electronic personhood.

Today, AI algorithms can analyze MRI and X-ray images to detect hidden tumors long before human radiologists can. They can scan a patient's genetic profile and, drawing on hundreds of thousands of past cases, work out in seconds which drug will be effective. But what if this supposedly "flawless" machine makes a mistake? If an AI labels an early-stage cancer a "benign cyst," the doctor trusts the algorithm and sends the patient home, and the patient dies months later, who will be held accountable in court?

Is it the software engineer who wrote the algorithm? The hospital that purchased the system? The doctor who approved the decision? Or is it an "unsolved" technological accident? This article examines the contested ground where medical malpractice meets technology law under four headings: "Background," "Global Legal Frameworks," "Clinical Implications," and "Conclusions."

The "Digital Third Mind" in the Exam Room and the Liability Dilemma

1. Background: The Anatomy of Medical Malpractice Law

To see why the problem is so intractable, we first need to look at how traditional medical malpractice claims work. Tort law in developed legal systems, including the Turkish Code of Obligations (TBK), generally requires "Fault" or "Negligence" before damages are awarded.

Traditionally, a physician is liable for a misdiagnosis or improper treatment only if they failed to meet the "Standard of Care": the level of care and skill that an average, reasonable physician in the same specialty would exercise in similar circumstances. A physician who departs from professional standards is at fault. Things change drastically, however, when the decision-maker is not a human but a Neural Network performing billions of calculations per second.

1.1. The "Black Box" Problem

Most modern healthcare AI systems rely on "Deep Learning." The system is fed millions of cancerous and non-cancerous lung scans and learns on its own which pixels indicate cancer. This process has a side effect, however: The Black Box Problem.

When an AI diagnoses a patient with cancer, even the engineer who built it cannot fully explain *how* it reached that conclusion or exactly which parameters it relied on. The law, however, runs on reasons. When a judge asks, "Why was this patient misdiagnosed?", the answer "The algorithm heavily weighted the 12th neuron in the 4th layer" means nothing in legal terms. Because the source of the fault cannot be known, the Burden of Proof collapses.

2. Global Legal Frameworks: Product Liability or Service Negligence?

Courts and legislators around the world are divided on whether to treat AI as a medical "tool" (like a scalpel) or as an active "consultant" (like another doctor).

2.1. Software as a Medical Device (SaMD)

In the US, the Food and Drug Administration (FDA) regulates diagnostic software as a medical device ("Software as a Medical Device", SaMD). In the EU, such software falls under the Medical Device Regulation (MDR, Regulation (EU) 2017/745); for most diagnostic software, conformity is assessed by notified bodies, not by the European Medicines Agency (EMA). If AI is a medical device, the applicable regime shifts from Medical Malpractice to Product Liability.

Product Liability rests on "Strict Liability." If a washing machine explodes because of a manufacturing defect and burns a house down, the consumer does not have to prove the manufacturer's "intent"; proving that the product was defective is enough. If AI is a product and harms a patient because of a coding error or a deficient dataset (e.g., the system misses skin cancer in a Black patient because it was only trained on data from Caucasian patients), the liability rests directly with the software company (the Developer).

2.2. Clinical Decision Support Systems (CDSS) and Physician Liability

To avoid multi-billion-dollar compensation claims, however, tech companies often insert the following clause into the End User License Agreements (EULA) of their AI systems: "This system is solely a Clinical Decision Support System (CDSS). The ultimate decision and diagnostic responsibility always rest with the physician."

3. Implications: How Will Medical Practice Transform?

This legal uncertainty is profoundly changing how doctors behave in clinical practice. The literature describes the results as "Defensive Medicine" and Automation Bias.

1. Automation Bias: People tend to believe that machines always calculate more accurately. An exhausted physician 36 hours into a shift who sees the label "98% benign tumor" on an AI screen will probably suppress their own doubts and defer to the machine.
2. Approving Errors Out of Fear of Liability: Suppose a doctor senses an abnormality, but the machine says, "The patient is completely healthy." If the doctor overrides the machine, decides to operate, and the surgery turns out to be unnecessary, hospital management and the court will ask the doctor: "Why didn't you listen to the million-dollar AI with a 99% accuracy rate?" This legal pressure pushes doctors to defer to the machine "to protect themselves legally," even when they disagree with it.
3. Data Poisoning and Cybersecurity: What if an AI system on hospital servers is hit by a cyberattack (Data Poisoning)? If a hacker secretly alters the algorithm's weights so that the system misreads certain heart rhythms, who bears criminal responsibility for the resulting homicide?

4. Conclusions: How Will the Law Catch Up to the Machine?

Traditional tort and malpractice rules are failing in the age of AI. Where human-machine interaction has become this complex, pinning the blame on a single person (the doctor or the coder) is unfair. Legal scholars are debating the following models:

• Mandatory AI Financial Liability Insurance: As with autonomous vehicles on the road, autonomous diagnostic systems in hospitals should be backed by mandatory insurance pools. When a patient is harmed, the loss should be paid promptly from this "No-Fault" compensation fund, rather than after a lengthy lawsuit over who was negligent.
• Electronic Personhood: Granting AI a limited legal status. Just as corporations (legal entities) can be sued despite not being human, certain autonomous medical AI systems could hold their own assets (insurance accounts) and be sued directly. The European Parliament, however, declined to recommend legal personality for AI systems in its October 2020 resolution on civil liability for AI.