ReviewThe Yale journal of biology and medicine2023
Artificial Intelligence to Improve Patient Understanding of Radiology Reports.
Review in The Yale journal of biology and medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis 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
26 citing papers in PubMed, 1 synthesis or guideline 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
- Evaluating large language models for lay summaries of radiology reports using tailored prompting strategies and mixed-method assessment.PLOS digital health · 2026Article
- Diagnostic accuracy of ChatGPT-5 in evaluating root canal treatment and periapical pathosis on periapical radiographs.Restorative dentistry & endodontics · 2026Article
- Artificial Intelligence (AI) Readiness to Support Evidence Synthesis by Workflow: Findings From a Review of Reviews.Campbell systematic reviews · 2026Article
- Psychological Impact of AI-Simplified Brain MRI Reports: A Randomized Trial of Patient Understanding, Anxiety, and Health Literacy.Journal of clinical medicine · 2026Article
- Artificial Intelligence in Patient-Centered Care and Macro-, Meso-, and Micro-Level Determinants of Rehumanization and Dehumanization: Qualitative Interview Study.Journal of medical Internet research · 2026Article
- Artificial Intelligence Across the Radiology Workflow: A Nine-Stage Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Evaluating Generative Artificial Intelligence as an Educational Tool for Radiology Resident Report Drafting.Journal of the American College of Radiology : JACR · 2026Article
- The use of artificial intelligence in healthcare as perceived by the citizens and patients: a narrative review of the literature.European journal of public health · 2025Review
- Comparative Evaluation of Large Language Models in Explaining Radiology Reports: Expert Assessment of Readability, Understandability, and Communication Features.Insights into imaging · 2025Article
- Large language models for patient education prior to interventional radiology procedures: a comparative study.CVIR endovascular · 2025Article
- AI-Generated Patient-Friendly MRI Fistula Summaries: A Pilot Randomised Study.Journal of imaging · 2025Article
- Evaluating the Quality and Understandability of Radiology Report Summaries Generated by ChatGPT: Survey Study.JMIR formative research · 2025Article
- CareAssist GPT improves patient user experience with a patient centered approach to computer aided diagnosis.Scientific reports · 2025Article
- AI-driven simplification of surgical reports in gynecologic oncology: A potential tool for patient education.Acta obstetricia et gynecologica Scandinavica · 2025Article
- Comparison of Multiple State-of-the-Art Large Language Models for Patient Education Prior to CT and MRI Examinations.Journal of personalized medicine · 2025Article
- Review
- Performance of ChatGPT and Microsoft Copilot in Bing in answering obstetric ultrasound questions and analyzing obstetric ultrasound reports.Scientific reports · 2025Article
- Article
- Generating colloquial radiology reports with large language models.Journal of the American Medical Informatics Association : JAMIA · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Diagnostic imaging reports are generally written with a target audience of other providers. As a result, the reports are written with medical jargon and technical detail to ensure accurate communication. With implementation of the 21st Century Cures Act, patients have greater and quicker access to their imaging reports, but these reports are still written above the comprehension level of the average patient. Consequently, many patients have requested reports to be conveyed in language accessible to them. Numerous studies have shown that improving patient understanding of their condition results in better outcomes, so driving comprehension of imaging reports is essential. Summary statements, second reports, and the inclusion of the radiologist's phone number have been proposed, but these solutions have implications for radiologist workflow. Artificial intelligence (AI) has the potential to simplify imaging reports without significant disruptions. Many AI technologies have been applied to radiology reports in the past for various clinical and research purposes, but patient focused solutions have largely been ignored. New natural language processing technologies and large language models (LLMs) have the potential to improve patient understanding of their imaging reports. However, LLMs are a nascent technology and significant research is required before LLM-driven report simplification is used in patient care.
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.