ArticleScientific reports2024
Patient-centered radiology reports with generative artificial intelligence: adding value to radiology reporting.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 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
49 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
- Radiological Reporting Beyond Written Text: Evaluation of the Dynamic Video Reporting Model in Venous Doppler Ultrasonography from Patient and Surgeon Perspectives.Journal of imaging informatics in medicine · 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
- Large Language Model Translation of BI-RADS Breast Imaging Reports into Arabic: A Blinded Expert Evaluation of Diagnostic Communication Safety.Diagnostics (Basel, Switzerland) · 2026Article
- Assessing family engagement and acceptance of patient-friendly radiology reports at two academic children's hospitals.Pediatric radiology · 2026Article
- Development and Clinical Evaluation of a Large Language Model-Based System for Generating Patient-Friendly Echocardiography Reports: Two-Stage Retrospective Validation and Prospective Survey Study.Journal of medical Internet research · 2026Article
- Article
- Simplifying radiology reports with large language models: privacy-compliant open- versus closed-weight models.European radiology · 2026Article
- Evaluation of Context-Aware Prompting Techniques for Classification of Tumor Response Categories in Radiology Reports Using Large Language Model.Journal of imaging informatics in medicine · 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 the potential of artificial intelligence and machine learning in orthopaedic surgery.Bulletin of the Hospital for Joint Disease (2013) · 2026Review
- Patient-Facing Radiology Communication with LLMs: Calibration Deficit and the Metadata Paradox.Healthcare (Basel, Switzerland) · 2026Article
- Patient-friendly simplification and translation of neuroradiology impressions using artificial intelligence.Scientific reports · 2026Article
- Patient and lesion characteristics associated with follow-up completion for pancreatic cystic lesions detected on MRI.Abdominal radiology (New York) · 2026Article
- Evaluating Generative Artificial Intelligence as an Educational Tool for Radiology Resident Report Drafting.Journal of the American College of Radiology : JACR · 2026Article
- Review
- Patient and clinician engagement with generative artificial intelligence (GenAI): A scoping review of implications for patient-centered communication.Patient education and counseling · 2026Article
- From Artificial Intelligence to Robotics: How to Navigate Technological Innovation in Radiology With the Gartner Hype Cycle.Academic radiology · 2026Review
- Patients and Caregivers Leveraging AI to Improve Their Health Care Journey: Case Study and Lessons Learned.Journal of participatory medicine · 2026Article
- Implementing generative artificial intelligence in precision oncology: safety, governance, and significance.Journal of hematology & oncology · 2026Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The purposes were to assess the efficacy of AI-generated radiology reports in terms of report summary, patient-friendliness, and recommendations and to evaluate the consistent performance of report quality and accuracy, contributing to the advancement of radiology workflow. Total 685 spine MRI reports were retrieved from our hospital database. AI-generated radiology reports were generated in three formats: (1) summary reports, (2) patient-friendly reports, and (3) recommendations. The occurrence of artificial hallucinations was evaluated in the AI-generated reports. Two radiologists conducted qualitative and quantitative assessments considering the original report as a standard reference. Two non-physician raters assessed their understanding of the content of original and patient-friendly reports using a 5-point Likert scale. The scoring of the AI-generated radiology reports were overall high average scores across all three formats. The average comprehension score for the original report was 2.71 ± 0.73, while the score for the patient-friendly reports significantly increased to 4.69 ± 0.48 (p < 0.001). There were 1.12% artificial hallucinations and 7.40% potentially harmful translations. In conclusion, the potential benefits of using generative AI assistants to generate these reports include improved report quality, greater efficiency in radiology workflow for producing summaries, patient-centered reports, and recommendations, and a move toward patient-centered radiology.
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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.