In the complex world of civil law, one constant remains: someone must be held accountable for damages. Determining who holds responsibility is one of the main reasons lawyers get involved, especially when patients seek compensation for harm or loss. However, as AI assumes increasingly central roles in the medical field, it raises a challenging new question: Who is liable when an AI system makes a mistake that endangers a patient’s life?

That’s a significant shift in how we think about responsibility in medicine. “When an algorithm makes a life-or-death call, it’s no longer just about human error—it’s about how we define accountability in a world where machines influence care,” says Dana Brooks of Fasig | Brooks Law Offices.

Let’s take a step back to understand the broader legal context.

Traditional Liability vs. Emerging AI Responsibility

In traditional malpractice cases, if a doctor’s negligence causes a patient harm, the doctor can be held personally liable. For example, if a physician fails to recognize the symptoms of a heart attack, they may face serious consequences, including loss of license. However, there are gray areas—cases where symptoms are subtle or easily misinterpreted, and even seasoned professionals might make the same mistake. These cases often lead to lengthy legal arguments in court.

Then there’s product liability law. If a faulty medical device causes harm, the manufacturer may be held accountable. Consider the Therac-25 radiation therapy incidents from the 1980s: a misprogrammed machine delivered radiation doses nearly 100 times higher than necessary, severely injuring patients. The manufacturer ultimately settled lawsuits for over $150 million.

Now, return to the question of AI. The challenge is that many AI systems in medicine operate as black-box models, meaning they make decisions based on data in ways even their creators don’t fully understand. This murky territory doesn’t fit neatly into traditional malpractice or product liability frameworks.

Doctors may argue that responsibility lies with the AI developers, while developers counter that the system’s performance depends on the data it receives and the conditions under which it’s used. That creates a tug-of-war, where neither party can be squarely blamed—yet the patient is still harmed.

Doctors may argue that responsibility lies with the AI developers, while developers counter that the system’s performance depends on the data it receives and the conditions under which it’s used. That creates a tug-of-war, where neither party can be squarely blamed—yet the patient is still harmed.

In many cases, AI tools are integrated into hospital systems without fully transparent disclosures of how the technology works or how its decisions are derived. If the system gives flawed recommendations or fails to detect a critical issue, legal teams may face a difficult task in determining where accountability begins and ends. Should it fall on the physician using the AI, the hospital deploying it, or the company that designed it?

For now, AI remains optional in patient care, but that’s changing. As adoption increases, hospitals and providers that avoid AI risk are seen as outdated. Medical facilities and personnel who decide against implementing AI may be considered outdated and less likely to be patronized. The pressure to adapt is real, and with it, the legal uncertainty grows.

Possible Paths Toward AI Accountability

Lawmakers, regulators, and courts are beginning to examine the legal implications of AI in healthcare. One potential solution is to create a no-fault compensation system, modeled after the National Vaccine Injury Compensation Program. Such a system would allow patients harmed by AI-assisted decisions to seek compensation without having to prove negligence, reducing the burden on the court system while still protecting innovation.

Another possibility is to assign a form of legal personhood to AI systems solely to hold them liable. While this concept is controversial, it would allow civil claims to be brought directly against the AI itself. Though it raises philosophical and technical questions, it could offer a clear path to accountability in cases where neither the physician nor the developer is entirely at fault.

Some legal scholars have also proposed shared liability models, in which fault is distributed among developers, healthcare institutions, and providers based on their roles in deploying or overseeing the AI. This would reflect the collaborative nature of modern medical decision-making and ensure that no single party bears the sole responsibility.

Regardless of the direction taken, one thing is clear: AI will continue to be a growing presence in healthcare, and legal frameworks must evolve accordingly. As its influence expands, so too must our understanding of responsibility, fairness, and patient protection in this new era of medicine.