When Patients Bring AI into the Exam Room

Managing Risk and Liability When Patients Rely on ChatGPT and Other AI Tools

Generative artificial intelligence (AI) and large language model tools such as ChatGPT and Claude are rapidly becoming part of the clinical encounter. Patients increasingly arrive at appointments with AI generated summaries of symptoms, differential diagnoses, and recommendations for testing or treatment. While seeking health information outside the clinical setting is not new, AI differs from earlier tools by offering information in a confident, conversational, and personalized manner—often conveying a sense of authority and precision that can complicate physician-patient interactions.¹

Several liability-relevant dynamics are emerging:

• Explicit Requests for Testing or Treatment: AI tools frequently suggest diagnostic studies (e.g., tilt table testing, advanced imaging, lab panels) without weighing patient-specific probability, sequence, or resource constraints. ¹
• Perceived Authority of AI Output: The polished tone and medical fluency of large language models can lead patients to overestimate accuracy and underestimate uncertainty. ¹
• Heightened Sensitivity to Refusal: When clinicians decline AI-suggested interventions, patients may interpret the decision as disregarded rather than clinical judgment—especially if communication is rushed or overly technical. ¹
• From a claims standpoint, these encounters increase exposure to allegations of failure to order tests, failure to diagnose, or lack of informed decision-making—even when care aligns with evidence-based guidelines.

COMMUNICATION AS PRIMARY RISK-REDUCTION TOOL

The literature underscores a central finding relevant to risk management: patients value being heard and recognized at least as much as they value clinical accuracy. In medicolegal terms, acknowledgment is not a courtesy—it is a protective behavior. ¹

INTEGRATING ACCURATE AI-GENERATED INFORMATION SAFELY

When AI-generated content aligns broadly with medical knowledge, it can be incorporated into the encounter in a way that supports clinical authority and reduces risk.

Best practices include:

• Acknowledge partial accuracy
  • “That test is used in certain situations, and it’s reasonable to ask about it.”
• Reframe around individualized risk assessment
  • Emphasize probability, timing, and clinical context rather than absolutes.
• Clarify sequencing and thresholds
  • Many liability claims arise not from refusing tests outright but from failing to explain why now is not the right time.

Documenting this discussion demonstrates that the clinician considered the patient’s input and applied professional judgment—an important defense if decisions are later questioned.

ADDRESSING INACCURATE OR NON-INDICATED RECOMMENDATIONS

When AI-suggested care conflicts with evidence-based practice, resource availability, or patient safety, outright dismissal increases risk. Risk-aware strategies include:

• Separate the Tool from the Patient—Rather than stating, “That’s wrong,” clinicians can explain:
  • AI tools list possibilities but do not assess likelihood.
  • They lack access to the patient’s examination, history, and test results.
  • They do not account for downstream harms of unnecessary testing.
• Explicitly Discuss Risks of Overuse—Documenting discussion of overdiagnosis, false positives, and cascading testing demonstrates informed clinical restraint—a key element in defending against “failure to test” claims.¹
• Be Transparent About System Constraints—AI tools do not factor in wait times, staffing limitations, or access barriers that affect care delivery. Naming these realities helps align patient expectations with feasible, safe care plans. ¹

DOCUMENTATION: A CRITICAL LINE OF DEFENSE

From a liability standpoint, documentation should reflect:

• That AI-generated information was discussed
• That patient concerns were acknowledged
• The clinical rationale for accepting or declining recommendations
• Evidence of shared decision making or informed refusal when applicable

Avoid chart language that implies irritation or dismissal (e.g., “patient insists,” “patient demands”). Neutral phrasing such as “patient inquired about…” or “patient requested discussion of…” is preferable.

REFRAMING THE PHYSICIAN’S ROLE TO REDUCE CLAIMS

Commentary from both U.S. and international clinicians suggests the physician role is shifting decisively from gatekeeper to guide. While this transition may feel uncomfortable, it aligns with established malpractice prevention principles: patients are less likely to sue clinicians they perceive as partners. ¹

AI-informed visits are not inherently higher risk—but poorly managed AI informed visits are. When patients feel heard, explanations are grounded in patient-specific reasoning, and decisions are transparently documented, the presence of AI becomes a manageable variable rather than a threat.

As one clinician observed, patients increasingly arrive armed with information to be heard. Meeting them with recognition rather than resistance preserves both the human core of medicine and the legal safeguards that support it. ¹

The information provided herein does not, and is not intended to constitute legal, medical, or other professional advice; instead, this information is for general informational purposes only. The specifics of each state’s laws and the specifics of each circumstance may impact its accuracy and applicability, therefore, the information should not be relied upon for medical, legal, or financial decisions and you should consult an appropriate professional for specific advice that pertains to your situation.

Article originally published in Copic’s Copiscope 2Q26 newsletter.