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The Quality of Information Produced by ChatGPT About Conditions Managed by Interventional Radiologists
Ruairidh Read1, Matthew Lukies2
1Box Hill Hospital, Eastern Health, Melbourne, Victoria, Australia.
Journal of Medical Imaging and Radiation Oncology
|August 9, 2025
Summary
Large language models (LLMs) like ChatGPT provide health information but lack source transparency and detail on treatment risks and benefits. Interventional Radiology (IR) professionals should guide patients on LLM-generated content.
Area of Science:
- Artificial Intelligence in Medicine
- Medical Information Dissemination
Background:
- Emerging AI-powered search engines and LLMs offer accessible health information.
- The accuracy and reliability of AI-generated health content remain uncertain.
- Interventional Radiology (IR) frequently manages conditions where AI-generated treatment information is sought.
Purpose of the Study:
- To evaluate the quality of ChatGPT-generated information on treatments for common Interventional Radiology (IR) conditions.
- To compare AI-generated medical information against established evidence bases.
Main Methods:
- ChatGPT was queried for the "best treatment" for six IR-managed conditions.
- Outputs were assessed using the DISCERN instrument.
- AI-generated information was benchmarked against the current evidence base.
Main Results:
- ChatGPT's mean score (1.3) was significantly lower than reference articles (3.8).
- Weaknesses included lack of source transparency, and insufficient detail on treatment risks, benefits, and mechanisms.
- Strengths included unbiased presentation of information.
Conclusions:
- LLMs represent a significant change in patient access to medical information.
- Understanding LLM strengths and weaknesses is crucial for Interventional Radiologists (IRs).
- Tailored patient communication and consultation with IRs are vital for navigating AI-generated health content.
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