ArticleEuropean radiology2024
ChatGPT makes medicine easy to swallow: an exploratory case study on simplified radiology reports.
Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 259 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
259 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
- The performance of large language models in dentomaxillofacial radiology: a systematic review.Dento maxillo facial radiology · 2025Pooled it
- GPT-4.1 and Llama 3.3 70 fail to detect clinically relevant errors in radiology reports in zero-shot evaluation.European radiology · 2026Article
- Fluent but not fail-safe: machine-translated radiology reports still need expert review.European radiology experimental · 2026Article
- Bias and Reliability of AI-Based Peer Review: A Comparative Study of ChatGPT and Claude Evaluating Scientific Abstracts.Journal of general internal medicine · 2026Article
- Can large language models accurately compute descriptive statistics from structured datasets? A comparative evaluation of ChatGPT and Claude.American heart journal plus : cardiology research and practice · 2026Article
- Interpreting antibody identification tests with large language models: Critical role of tabular data formatting.Transfusion · 2026Article
- An Evidence-Based Framework for Patient-Facing Artificial Intelligence Integration in the Emergency Department.Journal of the American College of Emergency Physicians open · 2026Article
- Improving patient communication and understanding of medical reports through text simplification with ChatGPT: a comparative study.Oral and maxillofacial surgery · 2026Article
- Can vision-language models estimate patient age? evidence from panoramic radiographs.Clinical oral investigations · 2026Article
- Less Is More: Radiologists Prefer Brief Narratives in LLM-Generated Clinical History Summaries.Journal of imaging informatics in medicine · 2026Article
- Evaluating large language models for lay summaries of radiology reports using tailored prompting strategies and mixed-method assessment.PLOS digital health · 2026Article
- Artificial Intelligence-enhanced cardiac MRI reporting: expert validation and patient-centered outcomes.The international journal of cardiovascular imaging · 2026Article
- Perceived communication quality of a standardized neuroradiology report following AI-based simplification: a randomized, blinded study.Insights into imaging · 2026Article
- Large language models and prostate MRI reporting: a stringent testbed for safe deployment under evolving AI, health-data, and cybersecurity regulation.Abdominal radiology (New York) · 2026Review
- Mapping the generative era: a bibliometric and specialty-focused analysis of large language models in healthcare.Visual computing for industry, biomedicine, and art · 2026Article
- Large language models for lumbar spondylolisthesis detection: a multi-center pilot comparative radiographic accuracy study.European journal of orthopaedic surgery & traumatology : orthopedie traumatologie · 2026Article
- The Performance of ChatGPT-4o and DeepSeek-R1 in Interpreting Thyroid Nodule Ultrasound Text Reports: Multicenter Study.Journal of medical Internet research · 2026Article
- Patient understanding of AI-simplified oncology imaging reports requires further validation.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Diagnostic Performance and Workup Efficiency of Large Language Models in Secondary Hypertension: A Blinded Comparative Study.Diagnostics (Basel, Switzerland) · 2026Article
199 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
objectivesTo assess the quality of simplified radiology reports generated with the large language model (LLM) ChatGPT and to discuss challenges and chances of ChatGPT-like LLMs for medical text simplification.
methodsIn this exploratory case study, a radiologist created three fictitious radiology reports which we simplified by prompting ChatGPT with "Explain this medical report to a child using simple language." In a questionnaire, we tasked 15 radiologists to rate the quality of the simplified radiology reports with respect to their factual correctness, completeness, and potential harm for patients. We used Likert scale analysis and inductive free-text categorization to assess the quality of the simplified reports.
resultsMost radiologists agreed that the simplified reports were factually correct, complete, and not potentially harmful to the patient. Nevertheless, instances of incorrect statements, missed relevant medical information, and potentially harmful passages were reported.
conclusionWhile we see a need for further adaption to the medical field, the initial insights of this study indicate a tremendous potential in using LLMs like ChatGPT to improve patient-centered care in radiology and other medical domains. CLINICAL RELEVANCE STATEMENT: Patients have started to use ChatGPT to simplify and explain their medical reports, which is expected to affect patient-doctor interaction. This phenomenon raises several opportunities and challenges for clinical routine. KEY POINTS: • Patients have started to use ChatGPT to simplify their medical reports, but their quality was unknown. • In a questionnaire, most participating radiologists overall asserted good quality to radiology reports simplified with ChatGPT. However, they also highlighted a notable presence of errors, potentially leading patients to draw harmful conclusions. • Large language models such as ChatGPT have vast potential to enhance patient-centered care in radiology and other medical domains. To realize this potential while minimizing harm, they need supervision by medical experts and adaption to the medical field.
Indexed as
Identifiers
What OpenQuestion holds
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.