ArticleJournal of the American Medical Informatics Association : JAMIA2024
Evaluation of GPT-4 ability to identify and generate patient instructions for actionable incidental radiology findings.
Article in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- An Evidence-Based Framework for Patient-Facing Artificial Intelligence Integration in the Emergency Department.Journal of the American College of Emergency Physicians open · 2026Article
- Automated generation of structured breast ultrasound reports using BreastViT and ChatGPT.BMC medical informatics and decision making · 2026Article
- Automated identification of incidentalomas requiring follow-up: A multi-anatomy evaluation of LLM-based and supervised approaches.Journal of biomedical informatics · 2026Article
- ED-Explain: Personalized Video Instructions for Patients Discharged from the Emergency Department.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- Improving radiology reporting accuracy: use of GPT-4 to reduce errors in reports.Abdominal radiology (New York) · 2026Article
- In-Context Learning with Large Language Models: A Simple and Effective Approach to Improve Radiology Report Labeling.Healthcare informatics research · 2025Article
- Weakly supervised language models for automated extraction of critical findings from radiology reports.NPJ digital medicine · 2025Article
- Open LLM-based actionable incidental finding extraction from [Frontiers in digital health · 2025Article
- Large language models in biomedicine and health: current research landscape and future directions.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Large language models in patient education: a scoping review of applications in medicine.Frontiers in medicine · 2024Article
- Mapping artificial intelligence models in emergency medicine: A scoping review on artificial intelligence performance in emergency care and education.Turkish journal of emergency medicineReview
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Authors and funding
12 authors.
Funding
Abstract
objectivesTo evaluate the proficiency of a HIPAA-compliant version of GPT-4 in identifying actionable, incidental findings from unstructured radiology reports of Emergency Department patients. To assess appropriateness of artificial intelligence (AI)-generated, patient-facing summaries of these findings. MATERIALS AND
methodsRadiology reports extracted from the electronic health record of a large academic medical center were manually reviewed to identify non-emergent, incidental findings with high likelihood of requiring follow-up, further sub-stratified as "definitely actionable" (DA) or "possibly actionable-clinical correlation" (PA-CC). Instruction prompts to GPT-4 were developed and iteratively optimized using a validation set of 50 reports. The optimized prompt was then applied to a test set of 430 unseen reports. GPT-4 performance was primarily graded on accuracy identifying either DA or PA-CC findings, then secondarily for DA findings alone. Outputs were reviewed for hallucinations. AI-generated patient-facing summaries were assessed for appropriateness via Likert scale.
resultsFor the primary outcome (DA or PA-CC), GPT-4 achieved 99.3% recall, 73.6% precision, and 84.5% F-1. For the secondary outcome (DA only), GPT-4 demonstrated 95.2% recall, 77.3% precision, and 85.3% F-1. No findings were "hallucinated" outright. However, 2.8% of cases included generated text about recommendations that were inferred without specific reference. The majority of True Positive AI-generated summaries required no or minor revision.
conclusionGPT-4 demonstrates proficiency in detecting actionable, incidental findings after refined instruction prompting. AI-generated patient instructions were most often appropriate, but rarely included inferred recommendations. While this technology shows promise to augment diagnostics, active clinician oversight via "human-in-the-loop" workflows remains critical for clinical implementation.
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