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Evaluation Of Gpt-4 Ability To Identify And Generate Patient Instructions For Actionable Incidental Radiology Findings.

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Evaluation Of Gpt-4 Ability To Identify And Generate Patient Instructions For Actionable Incidental Radiology Findings.

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Evaluation of GPT-4 ability to identify and generate patient instructions for actionable incidental radiology

Kar-Mun C Woo¹, Gregory W Simon¹, Olumide Akindutire¹

¹Ronald O. Perelman Department of Emergency Medicine, NYU Grossman School of Medicine, New York, NY 10016, United States.

Journal of the American Medical Informatics Association : JAMIA

|May 23, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

A HIPAA-compliant GPT-4 model effectively identifies actionable incidental findings in radiology reports. AI-generated patient summaries are mostly appropriate, but clinician oversight remains crucial for safe implementation.

Keywords:

ChatGPT GPT follow-up incidentaloma large language model radiology

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Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Radiology

Background:

Unstructured radiology reports contain incidental findings requiring follow-up.
Automated identification of these findings can improve patient care.
Large language models (LLMs) show potential for analyzing clinical text.

Purpose of the Study:

To evaluate a HIPAA-compliant GPT-4's accuracy in detecting actionable incidental findings in emergency department radiology reports.
To assess the appropriateness of AI-generated patient-facing summaries of these findings.

Main Methods:

A HIPAA-compliant GPT-4 model was developed and optimized using instruction prompts.
The model analyzed 430 unstructured radiology reports to identify 'definitely actionable' (DA) and 'possibly actionable-clinical correlation' (PA-CC) findings.

Performance was measured by recall, precision, and F-1 score; AI summaries were evaluated for appropriateness.

Main Results:

GPT-4 achieved high recall (99.3% for DA or PA-CC, 95.2% for DA only) in identifying actionable findings.
Precision varied (73.6% for DA or PA-CC, 77.3% for DA only), with minimal inferred recommendations (2.8%).
The majority of AI-generated patient summaries required no or minor revisions.

Conclusions:

Refined prompting enables GPT-4 to proficiently detect actionable incidental findings in radiology reports.
AI-generated summaries are largely appropriate, but may include inferred recommendations.
Human-in-the-loop workflows are essential for clinical implementation of this AI technology.