AI Misdiagnosis: Who Pays When Algorithms Err?

When an AI diagnostic tool makes a critical error and a patient gets hurt, who foots the bill? Is it the physician who trusted the algorithm, the developer who coded it, or the hospital system that rolled it out? As artificial intelligence works its way into clinical decisions, the field of medical malpractice is hitting complexities it’s never seen before, leaving both clinicians and the people building these tools in a tough spot. We’re digging into how the legal world is trying to make sense of algorithmic misdiagnosis, why current malpractice rules don’t fit, and what this all means for healthcare investors, legal teams, and medical group executives.

The Shifting Sands of Medical Malpractice: A Historical Context

For years, medical malpractice law has lived by the “reasonable physician” standard, where a doctor’s actions are judged against what another competent doctor would have done in the same situation. It’s all about human judgment. When software first showed up in clinics as clinical decision support (CDS), it was just an advisor. Doctors still had the final say, and if a software-guided decision went wrong, the blame typically fell on the physician for not using their own independent judgment. But the new generation of AI is different, especially diagnostic AI that spits out a definitive answer instead of just a list of suggestions. A classic CDS might say, “probable HFpEF, recommend referral,” but a true diagnostic AI might declare, “HFpEF confirmed.” This completely changes the doctor’s job from being the primary diagnostician to someone who just validates (or maybe oversees) what an algorithm says. This shift attacks the foundation of physician liability itself, because what exactly is “reasonable” oversight when an autonomous system does all the heavy diagnostic work?

The Physician’s Quandary: Reliance vs. Responsibility

The doctor’s problem is simple: they’re caught between using AI to be faster and more accurate, and being the one left holding the bag if it all goes wrong. Legal scholar Glenn Cohen, a key thinker at Harvard Law School’s Petrie-Flom Center, has written extensively about this liability maze. Cohen’s work shows just how unprepared our current malpractice system is for a scenario where an AI, regulated as a SaMD (Software as a Medical Device), makes a bad call that a doctor, even a diligent one, doesn’t catch. Think about an AI imaging tool cleared via the 510(k) pathway. It analyzes a scan and misses a subtle sign of a fast-growing tumor. The physician glances at the AI’s clean report, maybe does a quick human spot-check, and builds a treatment plan on that faulty assessment. When the patient is harmed, the malpractice suit will almost certainly name the physician. In this new world, though, the doctor’s defense will be that they correctly used a state-of-the-art tool and the real error was buried in the code. The American Medical Association (AMA) sees this coming. The AMA House of Delegates has passed resolutions insisting that physicians must keep ultimate responsibility for patient care, even with AI, but they also admit that doctors need proper training on the limits and biases of these systems. The AMA is gesturing toward a shared responsibility model, but the legal plumbing to make that model work just hasn’t been built yet. AMA policy on AI in medicine

The Manufacturer’s Exposure: Product Liability in the Digital Age

If the physician’s liability gets watered down because they were just relying on a bad algorithm, the legal spotlight swings directly onto the software developer. This is where product liability law enters the picture. Normally, makers of medical devices, including SaMD, face strict liability for design defects, manufacturing defects, and failing to warn users about risks. But trying to apply these old concepts to AI is a real headache:

• Design Defects: What’s a “design defect” in an AI that’s constantly learning and changing? Algorithmic drift, where a model’s accuracy gets worse over time as it sees new kinds of real-world data, makes this a nightmare. A model that was perfectly safe when it got its 510(k) clearance could become dangerously inaccurate a year later, with no change to its actual code. FDA guidance on AI/ML medical device change control
• Manufacturing Defects: An AI isn’t “manufactured” on an assembly line. Its creation is all about curating data, training the model, and validating its performance. An error here isn’t a physical flaw. It’s a biased dataset, an incomplete training library, or a sloppy validation method. Proving this kind of “manufacturing defect” means doing a deep forensic dive on the company’s data and development process, which is infinitely harder than just inspecting a faulty stent.
• Failure to Warn: Developers have to warn users about known risks. But what about unknown risks that emerge as the AI adapts, especially if it’s operating under a PCCP (Predetermined Change Control Plan)? The very nature of a dynamic AI means a complete list of warnings is always out of date. For investors, getting these details right is everything. A company building an AI diagnostic tool isn’t just working through regulatory paths like De Novo classification or Breakthrough Device Designation. It’s also bracing for product liability fights that go way beyond what hardware companies face. A good quality management system (QMS) and sticking to Good Machine Learning Practice (GMLP) are your best defense against future lawsuits, not just regulatory paperwork.
  

  The Institutional Role: Health Systems and Corporate Negligence

  It’s not just the doctor and the developer. The hospital or medical group that buys and implements the AI tool has its own skin in the game, facing liability for corporate or vicarious negligence. Health systems have a duty to make sure their staff are competent and their equipment is safe. When they plug an AI into their workflow, that list of duties gets longer:

• Due Diligence in Selection: Did the hospital actually do its homework before buying the AI? Did they check if it was clinically validated for their specific patients and how they planned to use it? Did they look at the vendor’s QMS, or check for HITRUST or SOC 2 compliance?
• Adequate Training and Implementation: Were doctors and nurses properly trained on what the AI could and couldn’t do? Did the rollout go smoothly and, importantly, did it still allow physicians to critically question the AI’s output?
• Monitoring and Oversight: Does the hospital have a system to watch the AI’s real-world performance over time, to spot things like algorithmic drift or emerging biases? Health systems are stuck between the pressure to adopt new tech and their fundamental duty to keep patients safe. As some states start floating AI liability “safe harbor” bills, the goal is often to protect doctors who use AI in good faith, which in turn pushes more of the risk onto the developers or forces everyone to get much clearer in their contracts. While states are passing a lot of AI-related health bills, actual liability safe harbors for providers are still rare, leaving the legal situation a patchwork of uncertainty. State legislative trends on AI liability
  

  Defining Boundaries: A Mandate for Portfolio Companies

  For Healthcare VCs and the lawyers advising them, the takeaway is blunt: the current fog around AI liability is a huge, unquantified risk. Any portfolio company building AI for healthcare has to get out in front of this. Just getting a 510(k) clearance or a CE Mark under EU MDR is nowhere near enough. Your enterprise software agreements need to draw hard lines around who is responsible for what. These agreements need to spell out:

• Indemnification Clauses: If there’s an AI-driven misdiagnosis, who pays for the defense and the damages?
• Data Rights and Responsibilities: Who owns the data the AI generates, and who’s on the hook for its quality and integrity, especially if the model is being retrained or is subject to algorithmic drift?
• Performance Guarantees and Monitoring: What are the specific, agreed-upon performance metrics for the AI in a clinical setting, and who is responsible for continuously monitoring and validating it?
• Transparency and Explainability: While it’s not a direct liability term, an AI that can explain its reasoning (explainable AI) is a powerful tool in a malpractice defense because it helps everyone understand where and why a failure might have occurred. Companies that can show they’ve thought through these legal risks and have built both contractual and technical firewalls will be far more attractive, de-risked investments. Being able to explain exactly how your product minimizes liability for both the doctor and the hospital, maybe through a rock-solid QMS or a well-documented PCCP, will make you stand out.
  

  Conclusion

  Bringing AI into the clinic has enormous potential, but it’s also creating a set of novel legal problems that we have to solve now. The old frameworks for medical malpractice and product liability are cracking under the pressure of algorithmic decisions. As experts like Glenn Cohen and organizations like the American Medical Association keep pointing out, we desperately need legal clarity so that patient safety and accountability don’t get left behind by technology. For investors and executives in the AI health space, getting a handle on this changing legal field isn’t just a compliance task, it’s about ensuring the long-term survival and ethical credibility of your entire company. ***

Methodology Note: This report is based on our review of legal scholarship, particularly from Harvard Law School’s Petrie-Flom Center, and policy statements from the American Medical Association. We verified data on legislative efforts and malpractice precedents using legal databases and industry reports.