ArticleInterventional pain medicine2025
Optimizing patient understanding of spine MRI reports using AI: A prospective single center study.
Article in Interventional pain medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Perceived communication quality of a standardized neuroradiology report following AI-based simplification: a randomized, blinded study.Insights into imaging · 2026Article
- Medical large language models and systems in the clinical application of spinal diseases: Current status, challenges, and future prospects.Journal of orthopaedic translation · 2026Review
- Article
- Chatbots in Radiology: Current Applications, Limitations and Future Directions of ChatGPT in Medical Imaging.Diagnostics (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Patient comprehension of spine MRI reports remains a significant challenge, potentially affecting healthcare engagement and outcomes. Artificial Intelligence (AI) may offer a solution for interpreting complex medical terminology into layman's terms language. Objective: To evaluate the effectiveness of AI-based interpretation of spine MRI reports in improving patient comprehension and satisfaction. Methods: A prospective, single-center survey study was conducted at a single institution's multidisciplinary pain and spine clinics from May 2024 to November 2024, enrolling 102 adult patients scheduled for spine MRI. Imaging reports were interpreted using a single AI-based Large Language Model (LLM) that is securely operated within the hospital's network, with interpretations independently reviewed by healthcare providers and research coordinators. A board-certified neuroradiologist evaluated the accuracy of AI interpretations using a standardized 5-point scale. We analyzed survey responses from participants who received both their original MRI reports and AI-interpreted versions, comparing comprehension, clarity, engagement, and satisfaction. Results: Participants reported higher comprehension with AI-interpreted MRI reports versus original radiology reports (8.50 ± 1.91 vs 6.56 ± 2.42; P < .001). AI interpretations received superior scores for clarity (8.57 ± 1.79 vs 6.96 ± 2.12; P < .001), understanding of medical conditions (7.75 ± 2.18 vs 6.27 ± 2.28; P < .001), and healthcare engagement (8.35 ± 2.00 vs 6.78 ± 2.48; P < .001). Accuracy assessment showed that 82.4 % of AI interpretations achieved high-quality ratings (≥4) [95 % CI: 69.7%-90.4 %], while 92.2 % were rated acceptable (≥3). Most participants (54.0 %) assigned the highest possible recommendation scores to AI interpretation. No significant differences were found between age groups and gender. Conclusions: AI-based interpretation of spine MRI reports significantly improved patient comprehension and satisfaction. Despite the promise of rapidly evolving AI-based technologies, a considerable percentage of AI interpretations were deemed to be inaccurate, warranting the need for further research.
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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 Socratic graph.