Evidence map›Paper›PMID 40611700›Full record

ArticleJMIR AI2025

Enhancing Magnetic Resonance Imaging (MRI) Report Comprehension in Spinal Trauma: Readability Analysis of AI-Generated Explanations for Thoracolumbar Fractures.

David C Sing, Kishan S Shah, Michael Pompliano, Paul H Yi, Calogero Velluto, Ali Bagheri, Robert K Eastlack, Stephen R Stephan, Gregory M Mundis

Abstract read
In one paragraph

Article in JMIR AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

David C SingDivision of Spine Surgery, Department of Orthopaedic Surgery, Scripps Clinic, 10710 N Torrey Pines Rd, La Jolla, CA, 92037, United States, 1 8585547988.ORCID http://orcid.org/0000-0002-5741-8411
Kishan S ShahDivision of Spine Surgery, Department of Orthopaedic Surgery, Scripps Clinic, 10710 N Torrey Pines Rd, La Jolla, CA, 92037, United States, 1 8585547988.ORCID http://orcid.org/0000-0002-0421-4829
Michael PomplianoDivision of Spine Surgery, Department of Orthopaedic Surgery, Scripps Clinic, 10710 N Torrey Pines Rd, La Jolla, CA, 92037, United States, 1 8585547988.ORCID http://orcid.org/0000-0002-3749-8581
Paul H YiDepartment of Radiology, St. Jude Children's Research Hospital, Memphis, TN, United States.ORCID http://orcid.org/0000-0001-9433-8093
Calogero VellutoDivision of Spine Surgery, Department of Orthopaedic Surgery, Scripps Clinic, 10710 N Torrey Pines Rd, La Jolla, CA, 92037, United States, 1 8585547988.ORCID http://orcid.org/0000-0002-3393-5748
Ali BagheriDivision of Spine Surgery, Department of Orthopaedic Surgery, Scripps Clinic, 10710 N Torrey Pines Rd, La Jolla, CA, 92037, United States, 1 8585547988.ORCID http://orcid.org/0009-0009-4875-0445
Robert K EastlackDivision of Spine Surgery, Department of Orthopaedic Surgery, Scripps Clinic, 10710 N Torrey Pines Rd, La Jolla, CA, 92037, United States, 1 8585547988.ORCID http://orcid.org/0000-0003-2359-1707
Stephen R StephanDivision of Spine Surgery, Department of Orthopaedic Surgery, Scripps Clinic, 10710 N Torrey Pines Rd, La Jolla, CA, 92037, United States, 1 8585547988.ORCID http://orcid.org/0000-0003-2598-0984
Gregory M MundisDivision of Spine Surgery, Department of Orthopaedic Surgery, Scripps Clinic, 10710 N Torrey Pines Rd, La Jolla, CA, 92037, United States, 1 8585547988.ORCID http://orcid.org/0000-0002-1483-7245

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Magnetic resonance imaging (MRI) reports are challenging for patients to interpret and may subject patients to unnecessary anxiety. The advent of advanced artificial intelligence (AI) large language models (LLMs), such as GPT-4o, hold promise for translating complex medical information into layman terms. Objective: This paper aims to evaluate the accuracy, helpfulness, and readability of GPT-4o in explaining MRI reports of patients with thoracolumbar fractures. Methods: MRI reports of 20 patients presenting with thoracic or lumbar vertebral body fractures were obtained. GPT-4o was prompted to explain the MRI report in layman's terms. The generated explanations were then presented to 7 board-certified spine surgeons for evaluation on the reports' helpfulness and accuracy. The MRI report text and GPT-4o explanations were then analyzed to grade the readability of the texts using the Flesch Readability Ease Score (FRES) and Flesch-Kincaid Grade Level (FKGL) Scale. Results: The layman explanations provided by GPT-4o were found to be helpful by all surgeons in 17 cases, with 6 of 7 surgeons finding the information helpful in the remaining 3 cases. ChatGPT-generated layman reports were rated as "accurate" by all 7 surgeons in 11/20 cases (55%). In an additional 5/20 cases (25%), 6 out of 7 surgeons agreed on their accuracy. In the remaining 4/20 cases (20%), accuracy ratings varied, with 4 or 5 surgeons considering them accurate. Review of surgeon feedback on inaccuracies revealed that the radiology reports were often insufficiently detailed. The mean FRES score of the MRI reports was significantly lower than the GPT-4o explanations (32.15, SD 15.89 vs 53.9, SD 7.86; P<.001). The mean FKGL score of the MRI reports trended higher compared to the GPT-4o explanations (11th-12th grade vs 10th-11th grade level; P=.11). Conclusions: Overall helpfulness and readability ratings for AI-generated summaries of MRI reports were high, with few inaccuracies recorded. This study demonstrates the potential of GPT-4o to serve as a valuable tool for enhancing patient comprehension of MRI report findings.

Indexed as

AIartificial intelligenceChatGPTlarge language modelLLMmagnetic resonance imagingMRIorthopedic surgerypatient educationspine surgerythoracolumbar fracturetrauma

Identifiers

PMID40611700
PMCPMC12231343

What OpenQuestion holds

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Registered trials

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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.