Artificial intelligence (AI) is rapidly becoming part of everyday medicine, helping with tasks such as taking notes during visits and streamlining administrative tasks for clinicians, scanning images to flag potential issues, double-checking prescriptions, and predicting which patients may be at higher risk for complications, according to recent studies. AI is being adopted in medicine faster than laws and liability standards are being updated to address it, leaving physicians in private practice caught in the middle.
These tools promise to make life easier for doctors and improve patient safety. AI is also transforming healthcare delivery by supporting both clinical and administrative functions across the healthcare system. But they also leave a lot of unanswered questions. If an AI system gets something wrong, who’s on the hook? And how much can you really lean on technology when making clinical decisions? The rapid adoption of artificial intelligence in medical malpractice brings significant concerns, including legal and ethical challenges related to liability.
Some malpractice claims involving AI have already surfaced, but there’s still no clear playbook for how they’ll be handled in the United States. Insurers are starting to update coverage policies, while medical boards are being urged to develop oversight frameworks for AI in clinical practice.
This article breaks down what clinicians need to know about AI liability, how errors are viewed, and how to use these tools safely in everyday practice.
For a closer look at how Indigo is applying AI to simplify and personalize malpractice coverage, check out our article on Indigo Technologies.
Since electronic charting has always been one of the most time-consuming parts of clinical practice, artificial intelligence is being added to the tools doctors use to document care. Clinical documentation is crucial in medical malpractice cases, as it helps demonstrate patient harm, reconstruct clinical scenarios, and establish causality between medical errors and adverse outcomes. Incorporating AI into this process requires careful consideration of ethical, legal, and practical factors, such as transparency, patient informed consent, privacy, and potential biases.
These tools save time and help physicians stay focused on patient care. But using AI in documentation also changes the workflow behind a medical record, like who enters information, how it’s verified, and what accountability looks like if something is wrong.
Some artificial intelligence systems, called ambient scribes, can listen to a visit and generate a note in real time. They reduce administrative work by taking over the typing and dictation that usually happens after each appointment, allowing physicians to stay focused on patients. By automating documentation, these tools can enhance human interaction by freeing up time for direct patient engagement. When they work well, they help doctors avoid burnout and stay more focused on patients, which can lead to safer, more accurate care.
But they aren’t always reliable: Misheard words or missing context can introduce medical errors that affect patient safety or lead to malpractice claims. There are also legal concerns about who owns the data collected by these systems and how securely that information is stored or shared. Healthcare providers should always obtain informed consent before recording or transcribing a visit and confirm that the system is HIPAA compliant.
When artificial intelligence assists in clinical decision-making, documentation should reflect how it informed your reasoning and whether you accepted or rejected its recommendation. It is important to record any AI-generated insights in the clinical documentation to support transparency and accountability. If you chose not to follow the output, briefly note your reasoning. This shows that you exercised your own clinical judgment and maintained responsibility for the decision. Adding brief details about how the tool was used supports transparency and makes clear that clinical accountability rests with the physician, not the technology.
For more on building defensible notes in general, see our guide to charting by exception.
Any use of artificial intelligence in healthcare has to keep patient information private and secure. That means knowing where the system stores data, like on a local server or in the cloud, and who can access it. Only authorized staff should be able to view protected health information (PHI), and all activity involving patient data should be logged and monitored. Healthcare practices should have clear rules for how often AI software is reviewed or updated to make sure it stays accurate and compliant.
These steps help maintain compliance, protect patient safety, and support a stronger medical liability defense if documentation is ever questioned. They also align with what insurers are monitoring closely as part of evolving AI and insurance standards.
Artificial intelligence’s role in medicine is expanding beyond documentation tools and now influences how physicians diagnose and make treatment decisions, including the growing use of AI diagnosis in everyday clinical care.
These systems can now take on tasks that used to be handled solely by physicians, like reading images, flagging potential risks, and suggesting next steps in treatment, as well as analyzing medical images to assist in diagnosis. As its use grows, it brings new questions about accountability and legal risk when errors occur. Careful AI integration in clinical workflows is essential to support professional liability assessments and ensure a balanced partnership between AI tools and human expertise.
AI is reshaping diagnostic medicine and risk exposure. Utilizing AI to analyze large datasets can help identify patterns and potential causal relationships, but human judgment remains crucial for interpreting these insights and establishing causation in complex clinical scenarios.
Not all artificial intelligence tools in medicine are treated the same under the law. The U.S. Food and Drug Administration (FDA) regulates some as medical devices, while others remain unregulated clinical tools.
Under the FDA’s Clinical Decision Support guidance, AI that analyzes patient data to drive or inform treatment decisions may qualify as a medical device and must meet stricter safety and validation requirements. By contrast, non-device AI systems, such as tools that organize data or flag potential risks, typically fall under less stringent oversight.
This regulatory gray area has major implications for medical liability. If an FDA-regulated AI product fails, the manufacturer may share some responsibility under product liability law. But when the AI isn’t classified as a device, the physician’s clinical judgment usually determines who’s held accountable.
These distinctions also raise questions about federal preemption, which can affect when a patient is allowed to sue a device manufacturer if the AI product was FDA-approved. In many cases, even if the tool plays a role in patient harm, the law may not make it easy to bring a claim against the company.
Because artificial intelligence in healthcare is still relatively new, there’s little case law on how courts will handle AI-related malpractice claims, but existing legal frameworks give some clues.
Physicians are still expected to meet the standard of care by exercising independent judgment when using AI systems and not relying on them blindly. If an AI recommendation leads to a wrong diagnosis or treatment, courts will still focus on whether the physician used the same judgment a reasonable doctor would in that situation. In these cases, legal liability is determined by assessing who is responsible for the error, whether it is the physician, the AI developer, or the healthcare institution, and legal professionals, such as expert witnesses with both medical and AI expertise, play a crucial role in evaluating the AI system's role and the applicable standards.
If the AI system malfunctions because of a design flaw or produces inaccurate results, the claim could shift toward product liability, especially if the tool is marketed as a diagnostic aid.
Plaintiffs might argue design defect or failure to warn if a manufacturer didn’t disclose known limitations or update the software when new risks emerged. Hospitals and clinics may face separate exposure if they fail to provide proper training, credentialing, or ongoing monitoring of how clinicians use these tools.
Artificial intelligence-assisted diagnostic tools are already being adopted across radiology, dermatology, cardiology, and oncology. While most perform well, errors can still happen when the AI gets the analysis wrong or recommends something that doesn’t fit the patient’s situation.
Research has shown that some AI tools for chest X-rays can misclassify pneumonia risk when the training data are biased or not representative. Biased data in AI-driven diagnostics can lead to errors in diagnosis or treatment decisions, particularly affecting marginalized patient populations. Recognizing and addressing these biases is crucial to improve AI reliability and fairness in medical assessments. Studies in dermatology have also found that consumer-facing apps may overstate malignancy risk for benign lesions.
Even when these tools improve detection overall, they can still create liability risks if physicians don’t verify the results or document their reasoning for accepting or overriding the AI’s findings.
For a deeper look at how AI is reshaping diagnostic medicine and risk exposure, see our AI diagnosis article.
Artificial intelligence is also influencing how courts and medical boards think about the standard of care in medical malpractice cases. As AI systems become more common in practice, expectations for what counts as “reasonable care” are beginning to shift. However, human expertise remains crucial for interpreting AI recommendations, making clinical judgments, and validating the relevance of AI-generated insights in complex cases.
As AI tools become more sophisticated, the legal implications of using or not using them will continue to evolve. Human judgment is essential in ensuring that liability assessments remain fair and accurate, as it addresses the limitations of AI in nuanced evaluation and contextual understanding.
Historically, the standard of care was based on customary practice, meaning the diagnostic and treatment steps most clinicians in a specialty typically used at the time. But newer legal frameworks, including recent discussions in the American Law Institute (ALI) Restatements, place more emphasis on what a reasonable healthcare provider should do, even if it departs from custom.
Increasingly, machine learning models are influencing how reasonable care is defined in medical malpractice cases, as courts and legal experts consider how these algorithms analyze conduct, establish causation, and assist expert witnesses.
In the context of AI, this matters because courts may ask:
As AI tools become more integrated into clinical medicine, the legal implications of using or not using them will continue to evolve.
Right now, physicians can’t be faulted for choosing not to use artificial intelligence tools. But that could change if a technology becomes well-validated and widely accepted in medical practice.
For example:
In that situation, failing to use the tool could eventually be seen as falling below the standard of care, particularly if it might have prevented patient harm. In future legal cases, this could be interpreted as medical negligence, as courts may view the omission as a failure to meet evolving standards in patient care.
This pattern is similar to earlier shifts in medicine. Pulse oximetry, for example, became a standard of care in anesthesia by the late 1980s, and tools like electrocardiogram (EKG) monitoring are now embedded in routine medical practice.
As AI adoption grows, healthcare providers will need to track how their specialty defines reasonable care to avoid a potential breach of duty.
Not every physician has access to artificial intelligence resources. Smaller practices, rural clinics, or under-resourced groups may lack the budget, training, or infrastructure to implement these tools, even when healthcare systems or academic centers consider them routine.
To protect against professional liability risks in these situations, smaller practices can:
Courts generally assess liability based on what a reasonable clinician would do with the resources they actually have, not those available to the largest health systems. Good documentation and transparent decision-making help ensure fairness when access to resources varies across practices. However, disparities in AI access can introduce additional medico-legal challenges, as differences in available technology may complicate questions of responsibility, liability, and standard of care.
Artificial intelligence decision-support tools don’t diagnose conditions on their own. Instead, they highlight what the system believes needs attention first, which can shape how quickly a clinician responds in time-sensitive situations.
These systems can improve safety when used well, but they also create exposure when clinicians rely on them too heavily or when the technology behaves unpredictably. This reliance can lead to unintended consequences, such as ethical, legal, or patient safety risks that may not be immediately apparent.
Even with advanced artificial intelligence in the workflow, physicians are still expected to exercise independent judgment. Human intervention remains crucial for ensuring the safe, ethical, and accountable use of AI in clinical settings. The biggest risk is automation bias, which can creep in when an artificial intelligence output feels authoritative and convenient, leading clinicians to accept it without the usual amount of checking or reasoning. To reduce exposure, clinicians need to:
Short documentation explaining why the clinician agreed or disagreed with an AI suggestion can significantly strengthen the medical record in a malpractice case. It shows that the clinician, and not the software, made the final call.
Some situations become higher risk when AI is involved because decisions need to be made quickly, and small errors can have an immediate impact. Examples include:
When the technology triggers an urgent recommendation, like suspected sepsis or a high-risk triage level, clinicians need to be able to explain whether they accepted it or chose to disagree with it, and why.
Even well-designed artificial intelligence systems can increase liability risk if they’re not implemented or monitored correctly. Common problems include:
Hospitals and practices should treat clinical AI the same way they treat any high-impact medical technology: with clear training requirements, documented validation, and periodic review to confirm that the tool is still performing as expected.
As artificial intelligence tools become increasingly integrated into daily clinical decisions, physicians, insurers, and regulators are paying closer attention to how these systems arrive at their recommendations and what that means for AI malpractice liability. The question is not only what the AI recommended, but why.
Demystifying medico-legal challenges related to AI in clinical practice is essential, as it helps address legal responsibilities, ethical considerations, and risk management when integrating AI technologies. When the reasoning behind an artificial intelligence output is unclear, it becomes more challenging for clinicians to evaluate it, document their judgment, and defend their care if it’s later questioned.
Even if a clinician is not expected to understand every technical detail of a proprietary algorithm, the vendor should still provide clear documentation that explains how the tool works, what its limits are, and how to use it safely. However, the risks and limitations of AI autonomy in legal medicine mean that relying solely on autonomous AI systems for liability assessments can be problematic; human oversight remains crucial to ensure accuracy, reduce bias, and maintain credibility in professional liability evaluations.
Many artificial intelligence systems operate as “black boxes,” meaning they generate recommendations, risk scores, or alerts without showing how they reached that conclusion. This lack of visibility creates several issues:
For juries and regulators, this creates uncertainty: Was the clinician using independent judgment, or relying on a tool that no one could fully interpret?
Because of this, practices should ask AI vendors clear questions before adopting or relying on any tool:
Even if you can’t see every step the AI took to reach a recommendation, the vendor should still provide clear documentation that explains how the tool works, what its limits are, and how to use it safely.
As artificial intelligence becomes more common in everyday clinical workflows, patients are beginning to ask when and how it’s being used in their care. While physicians don’t need to get into technical details, they do need to communicate AI involvement when it has a meaningful effect on the diagnosis, treatment plan, or risk assessment.
A simple, non-overwhelming way to explain this might be:
“I use a tool that highlights patterns in your labs and imaging and may flag things for us to double-check. I always review its suggestions myself and decide what’s appropriate based on your full clinical picture.”
Clear phrasing reassures patients that AI tools in healthcare support the clinician’s judgment, but don’t replace it. This transparency also strengthens trust, and it can help prevent misunderstandings if a medical malpractice claim later questions how an AI suggestion influenced the patient’s care.
Beyond understanding the artificial intelligence's recommendations, physicians and practices also need clear records showing which version of the tool was used, when it was updated, and how it produced its outputs at the time of care.
These records can become critical in AI malpractice claims, especially when questions arise about artificial intelligence errors, model drift, or unvalidated updates.
Key elements include:
Artificial intelligence can improve safety, but its mistakes tend to follow the same recognizable patterns. Medical AI systems are subject to unique error types, such as algorithmic bias and data misinterpretation, which can directly impact liability in malpractice cases. Understanding how those errors occur and how courts may interpret them helps clinicians utilize artificial intelligence tools in ways that reduce malpractice exposure.
Most artificial intelligence errors fall into a few repeatable categories:
The most common artificial intelligence-related malpractice scenarios mirror traditional claims, but with an AI twist:
The takeaway is that artificial intelligence errors become malpractice issues only when clinical judgment is impaired or overlooked as a result. When clinicians verify unusual outputs and document their reasoning, these scenarios are far less likely to lead to legal exposure.
Most artificial intelligence-related malpractice defenses come down to showing that the clinician exercised reasonable judgment and didn’t delegate medical decision-making to a tool.
Helpful defense elements include:
For a closer look at how different malpractice scenarios are usually evaluated, see our types of medical malpractice article.
Artificial intelligence bias shows up when a tool is more accurate for some patients than others, for example, it works well on the groups it was trained on, but makes more mistakes with patients of different ages, skin tones, genders, or medical backgrounds.
When this happens, the outputs can become inconsistent or inaccurate, which raises both patient-safety concerns and malpractice exposure. This has significant implications for public health and health outcomes, as biased AI can negatively affect population-level health initiatives and lead to disparities in treatment results. Courts and regulators are watching this closely as AI becomes part of everyday care.
Artificial intelligence systems learn patterns from the data they were trained on. If the training data didn’t include a wide range of patient types across age, race, ethnicity, gender, language, or comorbidities, the tool may perform well for some patients but poorly for others.
Examples include:
When these errors cause an artificial intelligence tool to misclassify patients, it can lead to missed diagnoses, delayed treatment, or inconsistent care across different groups. That creates a clear pathway for AI malpractice claims and raises questions about whether relying on the tool met the standard of care.
Because physicians are still responsible for independent clinical judgment, practices need systems in place to make sure the artificial intelligence tools they use are performing properly across their entire patient population.
Helpful steps include:
AI in healthcare liability often depends on whether the clinician relied on a tool in a way that was reasonable given its known limits. Showing that your practice evaluates and documents fairness goes a long way toward demonstrating responsible use.
For more on how disparities affect patient outcomes and legal exposure, see our minority healthcare article.
Artificial intelligence in healthcare is advancing faster than the law. Without one clear AI statute, clinicians are practicing in a legal environment where different rules overlap and evolve. Understanding the environment helps physicians use AI safely and stay aligned with the standard of care. However, the rapid adoption of AI brings significant legal challenges, legal risks, and medico-legal implications, including difficulties in assigning liability, managing regulatory requirements, and addressing ethical concerns related to AI-driven decisions. These issues affect not just physicians but all healthcare professionals involved in patient care.
The FDA’s Clinical Decision Support guidance remains the main source of direction on how AI should be used in care. Tools that help clinicians organize information are generally treated as non-devices, while AI that influences diagnosis or treatment may fall under medical-device rules. Some systems may even be classified as AI devices, which brings additional requirements for validation and monitoring. This matters for AI malpractice because device-level tools come with validation, monitoring, and clearer accountability expectations.
Civil rights rules are also shaping AI risk. The U.S. Department of Health and Human Services (HHS) updated its Section 1557 regulations to ban discrimination caused by algorithms, which means AI bias is something clinicians have to pay attention to because it affects both compliance and patient safety.
States are beginning to set their own expectations around AI use. Georgia’s recent legislation limits how automated systems can influence healthcare decisions, and California’s AB 2013 pushes for more transparency in how AI models are trained and used. For practices, the message is simple: you’re expected to know what the tool does, where the data comes from, and how it affects different patients. Many of these expectations also extend to AI developers, who may need to show how their systems were tested and updated.
Because there aren’t many artificial intelligence malpractice cases yet, courts are leaning on older cases about software mistakes and electronic health record (EHR) problems to guide their thinking.
Courts generally expect the clinician to be the final decision-maker, which means artificial intelligence outputs are treated as advice and not instructions. These questions still fall under familiar principles of malpractice law, even when AI contributes to the decision. If an AI output doesn’t fit the clinical picture and the clinician follows it anyway, the case is usually framed as a deviation from the standard of care. Courts also lean on existing tort law to analyze negligence and causation when clinical AI tools are involved.
When the issue is caused by the artificial intelligence tool rather than the clinician, patients may pursue product-liability claims or argue that the hospital didn’t oversee the technology well. Questions about AI liability now sit at the intersection of clinical care and legal medicine, and the expectations around them continue to shift. Courts are paying close attention to how medical teams document their use of AI, especially when there are audit logs showing whether an alert fired and how quickly the doctor acted.
Different liability models, such as vicarious liability, product liability, and shared responsibility among physicians, manufacturers, and hospitals, are being considered as courts determine how to apply these frameworks to AI use in medical practice.
Because the legal landscape is still developing, most of the practical safeguards come from your contracts and how your practice governs AI use.
Vendor contracts should clarify:
Even small practices benefit from basic oversight structures. Someone needs to review new artificial intelligence tools and make sure they actually work in your own practice and monitor performance over time. Simple steps like audit checks, update review, and clear documentation policies help reduce AI risk and show that the practice used the tool responsibly.
Even well-designed AI algorithms can behave unpredictably when the data they encounter differs from their training. These safeguards also improve professional liability assessment, especially when artificial intelligence contributes to the medical record.
Ultimately, even with vendor support, the clinician’s name is still on the chart. In short, strong contracts and good oversight make AI more of a support system and less of a liability concern.
As AI becomes part of everyday medical decision-making, insurers are rethinking how they assess risk, set premiums, and interpret claims. This shift is reshaping AI underwriting because AI-generated risk scores and decision-support tools now influence clinical judgment, which means carriers need to understand how they affect healthcare outcomes and potential liability.
Right now, most malpractice carriers are taking a cautious but flexible approach. Insurers aren’t refusing to cover AI-related events, but they are watching closely to see how AI risk affects safety, documentation, and the standard of care.
Key trends include:
As artificial intelligence becomes more common in clinical care, it’s also starting to shape how malpractice claims are reviewed. Carriers are using AI-driven analytics to spot patterns, flag inconsistencies, and evaluate documentation quality, but these systems still need to be monitored by a human, because analytics can misinterpret data or raise privacy questions if they pull in more information than intended.
Once a claim is filed, AI is mainly used to speed up the review process. Predictive analytics and AI fraud detection tools help carriers review claims faster by spotting outliers and pointing to possible gaps in the record. When used appropriately, they can reduce back-and-forth and shorten timelines for both the clinician and the insurer.
But these systems come with cautions:
Physicians should loop in their malpractice carrier before adopting any clinical artificial intelligence tool. Thoughtful communication can prevent coverage surprises later and help the insurer understand how AI fits into the practice’s workflow.
Key points to review include:
Open communication helps ensure you’re fully covered and that your carrier understands the AI tools shaping your practice.
Legal and regulatory expectations around clinical AI will continue to evolve. Physicians won’t be expected to understand every technical detail, but they will be expected to know each tool’s limits, keep clear documentation, and maintain independent judgment. Over the next few years, expect clearer rules, more transparency, and stronger direction on how AI malpractice and AI liability should be handled.
Legal expectations aren’t fixed yet and will keep changing as AI technologies become more common in everyday care. Over time, courts and regulators may begin treating certain well-validated systems as part of the standard of care. That doesn’t mean physicians must follow every AI recommendation, but it does mean they’ll need to understand how the tool works, what its limits are, and how to document their reasoning.
Policymakers are exploring ways to give clinicians clearer protections when they use well-validated AI tools correctly, along with new transparency expectations for how AI systems operate and clearer insurance language about how AI-related errors will be handled.
None of these are finalized, but the direction is clear: the healthcare system is moving toward more structure, clearer rules, and shared accountability between physicians, vendors, and insurers.
While the legal system catches up, physicians can take a few simple steps to reduce AI risk today:
These small habits go a long way in limiting exposure from AI errors, AI triage issues, AI-generated documentation problems, or unexpected AI failure modes.
AI’s value over the long run relies on making care safer, more efficient, and more consistent, while keeping clinical decisions in physicians’ hands. The larger aim is a system built on transparency, shared accountability, and trust, where artificial intelligence tools help prevent errors instead of adding new risks.
Looking ahead, the biggest advantage clinicians can have is staying tuned into how AI tools perform, how they’re evolving, and how AI liability rules are changing.
With the right oversight and clear documentation, AI in healthcare can strengthen, not complicate, clinical practice. Physicians who approach these tools thoughtfully will be well-positioned to benefit from them while keeping AI risk and liability in check.
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Image by Mohammed Haneefa Nizamudeen from iStock.
