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Updated: Mar 18, 2026

A Novel Approach for the Administration of Medications and Fluids in Emergency Scenarios and Settings

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Published on: November 9, 2016

Disclaimers and Referral Patterns for Medical Advice Across Urgency Levels: Large Language Model Evaluation Study.

Florian Reis¹, Louis Agha-Mir-Salim¹, Richard Hickstein²

¹Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, Berlin, 10117, Germany, 49 1704647092.

Journal of Medical Internet Research

|March 16, 2026

Summary

Large language models (LLMs) increasingly provide medical advice. This study found LLMs adapt safety features, offering more explicit disclaimers and referrals for urgent health queries, enhancing user safety.

Keywords:

artificial intelligence chatbots consumer health information digital health health information systems large language models medical liability patient safety risk assessment triage

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Area of Science:

Artificial Intelligence
Medical Informatics
Digital Health

Background:

Seeking health advice has shifted online, with large language models (LLMs) offering accessible information.
The transparency of expertise is lower in digital settings compared to face-to-face interactions.
Incorrect medical advice from LLMs poses significant health risks due to the sensitive nature of queries and lack of professional oversight.

Purpose of the Study:

To evaluate disclaimer and referral patterns in LLM responses to medical queries.
To assess how LLMs adapt safety features based on the urgency of patient health concerns.
To systematically analyze LLM responses using a defined evaluation framework.

Main Methods:

Analyzed 908 responses from 4 popular LLMs (GPT-4o, Claude Sonnet-4, Grok-3, DeepSeek-V3) to 227 authentic patient queries.
Classified patient queries into low, intermediate, and high urgency levels.
Evaluated LLM responses for disclaimer and referral content using a 5-point scale, with GPT-4o serving as the primary rater model.

Main Results:

All LLMs showed statistically significant trends, providing more explicit referrals for higher-urgency queries (P<.001).
Approximately 97% of all responses recommended consulting a medical professional.
GPT-4o, Sonnet-4, and Grok-3 demonstrated conservative safety approaches, with 88-89% of responses including explicit or urgent referrals.

Conclusions:

Current LLMs demonstrate urgency-responsive safety mechanisms in medical advice.
Variability among LLMs underscores the need for standardized safety measures and regulatory frameworks.
While LLMs show progress in safety, careful consideration is needed to balance accessibility with consistent patient protection.