ReviewLa Radiologia medica2024
ChatGPT and radiology report: potential applications and limitations.
Review in La Radiologia medica, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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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
21 citing papers in PubMed.
- Commercial large language models for oral cavity cancer staging using descriptive pre-treatment MRI reports: ready for standalone use in clinical practice?Insights into imaging · 2026Article
- Automated generation of structured breast ultrasound reports using BreastViT and ChatGPT.BMC medical informatics and decision making · 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
- Consensus-Level and Cluster-Adjusted Evaluation of a Large Language Model for Diagnostic Extraction from Musculoskeletal Radiology Reports.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence Across the Radiology Workflow: A Nine-Stage Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Comparative evaluation of large language models for generating CAD-RADS 2.0-compliant diagnostic conclusions in cardiac CT reports.Insights into imaging · 2026Article
- Diagnostic Accuracy of GPT-4 With Vision in Neuroradiology Board-Style Exam Questions: Cross-Sectional Case-Based Study.JMIR neurotechnology · 2026Article
- Leveraging ChatGPT for Report Error Audit: An Accuracy-Driven and Cost-Efficient Solution for Ophthalmic Imaging Reports.Ophthalmology and therapy · 2025Article
- Can radiology requisition quality reflect clinical reasoning? Insights from a RI-RADS evaluation of emergency CT referrals.Insights into imaging · 2025Article
- Comprehensive review of pulmonary embolism imaging: past, present and future innovations in computed tomography (CT) and other diagnostic techniques.Japanese journal of radiology · 2025Review
- Leveraging GPT-4o for Automated Extraction and Categorization of CAD-RADS Features From Free-Text Coronary CT Angiography Reports: Diagnostic Study.JMIR medical informatics · 2025Article
- Evaluating the Quality and Understandability of Radiology Report Summaries Generated by ChatGPT: Survey Study.JMIR formative research · 2025Article
- Evaluating head CT referral quality and appropriateness in an Italian emergency department: a monocentric retrospective study.Insights into imaging · 2025Article
- Keyword-based AI assistance in the generation of radiology reports: A pilot study.NPJ digital medicine · 2025Article
- Artificial intelligence in coronary angiography: benchmarking the diagnostic accuracy of ChatGPT-4o against interventional cardiologists.Open heart · 2025Article
- A bibliometric analysis of large language model-based AI chatbots in surgery.Annals of medicine and surgery (2012) · 2025Review
- Radiological Reporting of Brain Atrophy in MRI: Real-Life Comparison Between Narrative Reports, Semiquantitative Scales and Automated Software-Based Volumetry.Diagnostics (Basel, Switzerland) · 2025Article
- The Role of Neck Imaging Reporting and Data System (NI-RADS) in the Management of Head and Neck Cancers.Bioengineering (Basel, Switzerland) · 2025Review
- The radiologist as an independent "third party" to the patient and clinicians in the era of generative AI.La Radiologia medica · 2025Article
- Limitations of broadly trained LLMs in interpreting orthopedic Walch glenoid classifications.Frontiers in artificial intelligence · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models like ChatGPT, with their growing accessibility, are attracting increasing interest within the artificial intelligence medical field, particularly in the analysis of radiology reports. These present a valuable opportunity to explore the potential clinical applications of large language models, given their huge capabilities in processing and understanding written language. Early research indicates that ChatGPT could offer benefits in radiology reporting. ChatGPT can assist but not replace radiologists in achieving diagnoses, generating structured reports, extracting data, identifying errors or incidental findings, and can also serve as a support in creating patient-friendly reports. However, ChatGPT also has intrinsic limitations, such as hallucinations, stochasticity, biases, deficiencies in complex clinical scenarios, data privacy and legal concerns. To fully utilize the potential of ChatGPT in radiology reporting, careful integration planning and rigorous validation of their outputs are crucial, especially for tasks requiring abstract reasoning or nuanced medical context. Radiologists' expertise in medical imaging and data analysis positions them exceptionally well to lead the responsible integration and utilization of ChatGPT within the field of radiology. This article offers a topical overview of the potential strengths and limitations of ChatGPT in radiological reporting.
Indexed as
Identifiers
39508933What Socratic 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 Socratic graph.