Observational studyScientific reports2025
Application of artificial intelligence chatbots in interpreting magnetic resonance imaging reports: a comparative study.
Observational study in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 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
- Comparative evaluation of generative AI models for chest radiograph report generation in the emergency department.European radiology · 2026Article
- Performance of ChatGPT-4o, Claude 3 Opus, and DeepSeek-R1 in BI-RADS Category 4 Classification and Malignancy Prediction From Mammography Reports: Retrospective Diagnostic Study.JMIR medical informatics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Artificial intelligence (AI) chatbots have emerged as promising tools for enhancing medical communication, yet their efficacy in interpreting complex radiological reports remains underexplored. This study evaluates the performance of AI chatbots in translating magnetic resonance imaging (MRI) reports into patient-friendly language and providing clinical recommendations. A cross-sectional analysis was conducted on 6174 MRI reports from tumor patients across three hospitals. Two AI chatbots, GPT o1-preview (Chatbot 1) and Deepseek-R1 (Chatbot 2), were tasked with interpreting reports, classifying tumor characteristics, assessing surgical necessity, and suggesting treatments. Readability was measured using Flesch-Kincaid and Gunning Fog metrics, while accuracy was evaluated by medical reviewers. Statistical analyses included Friedman and Wilcoxon signed-rank tests. Both chatbots significantly improved readability, with Chatbot 2 achieving higher Flesch-Kincaid Reading Ease scores (median: 58.70 vs. 46.00, p < 0.001) and lower text complexity. Chatbot 2 outperformed Chatbot 1 in diagnostic accuracy (92.05% vs. 89.03% for tumor classification; 95.12% vs. 84.73% for surgical necessity, p < 0.001). Treatment recommendations from Chatbot 2 were more clinically relevant (98.10% acceptable vs. 75.41%), though both demonstrated high empathy (92.82-96.11%). Errors included misinterpretations of medical terminology and occasional hallucinations. AI chatbots, particularly Deepseek-R1, effectively enhance the readability and accuracy of MRI report interpretations for patients. However, physician oversight remains critical to mitigate errors. These tools hold potential to reduce healthcare burdens but require further refinement for clinical integration.
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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.