AI Charting Errors and Your Medical License

Emergency physicians already operate in the highest-risk medicolegal environment in clinical medicine. Diagnostic miss rates for ACS/MI, aortic dissection, pulmonary embolism, sepsis, and subarachnoid hemorrhage generate consistent litigation exposure. Layer in AI-generated documentation, AI-assisted diagnosis, and AI-recommended treatment plans, and the liability landscape becomes substantially more complex — in ways most emergency physicians are not yet prepared to navigate.

What follows is not legal advice. It is a clinician-to-clinician analysis of the emerging medicolegal terrain, written so you can have an informed conversation with your risk management team before you need it.

AI-Generated Documentation: The Attestation Problem

Ambient AI documentation tools — platforms (like Epic and DAX) that use audio capture and natural language processing to generate structured clinical notes — are entering emergency medicine practice at scale. The value proposition is genuine: reduced documentation burden, improved completeness, and time returned to direct patient care.

But every note you sign is a legal document representing your attestation of its accuracy. That principle is not new. What is new is the character of AI documentation errors compared to physician documentation errors.

When you dictate a note, your cognitive processes — memory of the encounter, clinical reasoning, professional judgment — are active in generating the content. When an AI generates a note, it produces output based on pattern recognition applied to audio or text input. It will occasionally hallucinate: generate plausible-sounding content that did not occur in the encounter. It will miss critical nuance. It will document negatives — “patient denied chest pain” — when the clinical encounter was considerably more ambiguous.

The physician who signs an AI-generated note without critical review is accepting legal ownership of errors they did not make and may not have caught. The standard of care question — did this physician meet the standard expected of a reasonable practitioner? — will increasingly include whether the physician critically reviewed AI-generated documentation before attestation. That expectation is not speculative; it is the direction medicolegal standards are already moving.

Diagnostic AI and the Evolving Standard of Care

The standard of care in emergency medicine is not static. It evolves with the tools available in clinical practice. As AI diagnostic support becomes widely deployed and widely used, the standard of care begins to incorporate its application. If an AI-assisted ECG analysis platform is active in your department and flags a STEMI equivalent that you did not act on because you did not review its output, your standard of care exposure becomes genuinely complex.

The converse is also emerging. If an AI tool generates a false positive that drives an unnecessary intervention — a false positive STEMI activation, a false positive PE probability that leads to thrombolysis — the liability question centers on whether the physician acted appropriately given both the AI output and the clinical picture, or whether they deferred excessively to the algorithm without independent clinical reasoning.

AI is not a shield from liability. In some circumstances, it increases exposure by raising the standard of care expectation for what a reasonable, AI-equipped physician would have known and acted upon. Understanding that dynamic — before an adverse event — is essential.

Informed Consent in the AI Era

Patients are increasingly aware that AI may be involved in their care, and the informed consent framework for AI-assisted diagnosis and treatment recommendations is moving toward explicit disclosure. The bioethics literature and emerging healthcare regulation both point in the same direction. Several states are already developing AI disclosure requirements in clinical settings.

Emergency medicine complicates this further. The informed consent process is already compressed by urgency — you are not going to explain your AI ECG platform to a STEMI patient before activating the cath lab. But the institutional and systemic disclosure frameworks need to be in place before they are required, and emergency physicians should be engaged in developing them rather than encountering them for the first time through a regulatory action or malpractice filing.

What You Should Be Doing Now

• Know which AI tools are deployed in your department and what they are — and are not — approved for. The liability exposure from using an AI tool outside its validated indication is categorically different from the exposure of using it appropriately within its scope. Your risk management team should maintain a catalog of deployed AI tools with their validation parameters and approved use cases.
• Establish a personal documentation review practice for AI-generated notes. This does not require reviewing every word, but it demands active clinical cognition — confirming that the note’s key clinical elements, decision points, and plan accurately reflect the actual encounter. A signature is an attestation, not a rubber stamp.
• Engage with your department’s AI governance process. If your department or health system does not have one, that is the first problem to solve. AI governance in healthcare is not an IT function — it is a clinical function, and emergency physicians need to be at the table. The physicians who shape these frameworks will be better protected than those who simply inherit them.

Dr. Chet's Take:

I direct three programs where AI deployment decisions carry immediate operational and clinical consequences—and where I'm ultimately accountable for adverse outcomes. That accountability is why I don't view AI governance as an IT checkbox; it's a command responsibility. When DAX or any diagnostic support tool goes live in my department, I need to know exactly what it was validated on, where it fails, and whether my team understands the difference between a tool that aids judgment and one that replaces it. The physicians who treat AI governance as something that happens to them—rather than something they lead—are accepting liability exposure they didn't create and may not be able to defend. The medicolegal terrain is shifting faster than most risk management teams are moving, and waiting for your hospital's legal department to catch up is a losing strategy.

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Dr. Chester “Chet” Shermer, MD, FACEP is a Professor of Emergency Medicine, Medical Director for Air Medical and Critical Care Transport programs, and a military medical commander with the Army National Guard. He is the founder of Global MedOps Command and the creator of AI in Emergency Medicine: Becoming AI Bulletproof.

AI Won’t Wait. Neither Should You.

The liability landscape described in this post is not a future problem — it is unfolding now, in your department, on your shifts. Emergency physicians who understand AI’s risks and capabilities will be positioned to lead. Those who don’t will be exposed. Consider enrolling my course: AI in Emergency Medicine: Becoming AI Bulletproof.

➤  Learn more: AI in Emergency Medicine: Becoming AI Bulletproof