ArticleCureus2023
Assessing ChatGPT's Proficiency in Simplifying Radiological Reports for Healthcare Professionals and Patients.
Article in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
38 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Large language models for simplifying radiology reports: a systematic review and meta-analysis of patient, public, and clinician evaluations.The Lancet. Digital health · 2026Pooled it
- Testing and Evaluation of Health Care Applications of Large Language Models: A Systematic Review.JAMA · 2025Pooled it
- Applications, Challenges, and Future Directions of Large Language Models in Health Care Communication: Scoping Review.Journal of medical Internet research · 2026Article
- Do you Really Want to Know-Patient and Physician Attitudes of Physicians and English-Proficient Asian Patients toward Direct Release of Radiology Reports in Singapore.Journal of imaging informatics in medicine · 2026Article
- Exploring Radiologists' Use of AI Chatbots for Assistance in Image Interpretation: Patterns of Use and Trust Evaluation.Journal of imaging informatics in medicine · 2026Article
- Patient and clinician engagement with generative artificial intelligence (GenAI): A scoping review of implications for patient-centered communication.Patient education and counseling · 2026Article
- Article
- Comparative assessment of large language models in diabetic foot infection management: alignment with IWGDF/IDSA guidelines.Frontiers in endocrinology · 2026Article
- Evaluating Consistency and Accuracy of GPT-4 Omni to Analyze Thyroid Ultrasound Features and ACR TR Categories to Aid Report GenerationCurrent medical imaging · 2026Article
- Large Language Models in Radiologist-Patient Communication: A Narrative Review for Clinical Practice.Cureus · 2026Review
- Trends and Trajectories in the Rise of Large Language Models in Radiology: Scoping Review.JMIR medical informatics · 2025Article
- Article
- Artificial Intelligence Techniques and Health Literacy: A Systematic Review.Mayo Clinic proceedings. Digital health · 2025Review
- Evaluating diagnostic accuracy of large language models in neuroradiology cases using image inputs from JAMA neurology and JAMA clinical challenges.Scientific reports · 2025Article
- Article
- Review
- Article
- Evaluating ChatGPT's ability to simplify scientific abstracts for clinicians and the public.Scientific reports · 2025Article
- Artificial intelligence-simplified information to advance reproductive genetic literacy and health equity.Human reproduction (Oxford, England) · 2025Observational
- Application of artificial intelligence chatbots in interpreting magnetic resonance imaging reports: a comparative study.Scientific reports · 2025Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Clear communication of radiological findings is crucial for effective healthcare decision-making. However, radiological reports are often complex with technical terminology, making them challenging for non-radiology healthcare professionals and patients to comprehend. Large language models like ChatGPT (Chat Generative Pre-trained Transformer, by OpenAI, San Francisco, CA) offer a potential solution by translating intricate reports into simplified language. This study aimed to assess the capability of ChatGPT-3.5 in simplifying radiological reports to facilitate improved understanding by healthcare professionals and patients. Materials and methods Nine radiological reports were taken for this study spanning various imaging modalities and medical conditions. These reports were used to ask ChatGPT a set of seven questions (describe the procedure, mention the key findings, express in a simple language, suggestions for further investigation, need of further investigation, grammatical or typing errors, and translation into Hindi). A total of eight radiologists rated the generated content in detailing, summarizing, simplifying content and language, factual correctness, further investigation, grammatical errors, and translation to Hindi. Results The highest score was obtained for detailing the report (94.17% accuracy) and the lowest score was for drawing conclusions for the patient (85% accuracy); case-wise scores were similar (p-value = 0.97). The Hindi translation by ChatGPT was not suitable for patient communication. Conclusion The current free version of ChatGPT-3.5 was able to simplify radiological reports effectively, removing technical jargon while preserving essential diagnostic information. The free version adeptly simplifies radiological reports, enhancing accessibility for healthcare professionals and patients. Hence, it has the potential to enhance medical communication, facilitating informed decision-making by healthcare professionals and patients.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.