Evidence map›Paper›PMID 37950763›Full record

ArticleArchives of orthopaedic and trauma surgery2024

Simplifying radiologic reports with natural language processing: a novel approach using ChatGPT in enhancing patient understanding of MRI results.

Sebastian Schmidt, Alexander Zimmerer, Tudor Cucos, Matthias Feucht, Luis Navas

Abstract read
PubMed Publisher
In one paragraph

Article in Archives of orthopaedic and trauma surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 1 pooled it
–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

25 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Article
  18. Review
  19. Assessing ChatGPT's summarization ofAbdominal radiology (New York) · 2025
    Article
  20. 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

5 authors.

Sebastian SchmidtDepartment of Orthopaedic and Trauma Surgery, Orthopädische Klinik Paulinenhilfe, Diakonieklinikum, Rosenbergstrasse 38, 70192, Stuttgart, Germany. schmidt.sebastian@fn.de.
Alexander ZimmererDepartment of Orthopaedic and Trauma Surgery, Orthopädische Klinik Paulinenhilfe, Diakonieklinikum, Rosenbergstrasse 38, 70192, Stuttgart, Germany.
Tudor CucosDepartment of Radiology, ViDia Christliche Kliniken Karlsruhe, Steinhäuser Straße 18, 76135, Karlsruhe, Germany.
Matthias FeuchtDepartment of Orthopaedic and Trauma Surgery, Orthopädische Klinik Paulinenhilfe, Diakonieklinikum, Rosenbergstrasse 38, 70192, Stuttgart, Germany.
Luis NavasDepartment of Orthopaedic and Trauma Surgery, Orthopädische Klinik Paulinenhilfe, Diakonieklinikum, Rosenbergstrasse 38, 70192, Stuttgart, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe aim of this prospective cohort study was to assess the factual accuracy, completeness of medical information, and potential harmfulness of incorrect conclusions by medical professionals in automatically generated texts of varying complexity (1) using ChatGPT, Furthermore, patients without a medical background were asked to evaluate comprehensibility, information density, and conclusion possibilities (2).

methodsIn the study, five different simplified versions of MRI findings of the knee of different complexity (A: simple, B: moderate, C: complex) were each created using ChatGPT. Subsequently, a group of four medical professionals (two orthopedic surgeons and two radiologists) and a group of 20 consecutive patients evaluated the created reports. For this purpose, all participants received a group of simplified reports (simple, moderate, and severe) at intervals of 1 week each for their respective evaluation using a specific questionnaire. Each questionnaire consisted of the original report, the simplified report, and a series of statements to assess the quality of the simplified reports. Participants were asked to rate their level of agreement with a five-point Likert scale.

resultsThe evaluation of the medical specialists showed that the findings produced were consistent in quality depending on their complexity. Factual correctness, reproduction of relevant information and comprehensibility for patients were rated on average as "Agree". The question about possible harm resulted in an average of "Disagree". The evaluation of patients also revealed consistent quality of reports, depending on complexity. Simplicity of word choice and sentence structure was rated "Agree" on average, with significant differences between simple and complex findings (p = 0.0039) as well as between moderate and complex findings (p = 0.0222). Participants reported being significantly better at knowing what the text was about (p = 0.001) and drawing the correct conclusions the more simplified the report of findings was (p = 0.013829). The question of whether the text informed them as well as a healthcare professional was answered as "Neutral" across all findings.

conclusionBy using ChatGPT, MRI reports can be simplified automatically with consistent quality so that the relevant information is understandable to patients. However, a report generated in this way does not replace a thorough discussion between specialist and patient.

Indexed as

Health PersonnelNatural Language ProcessingHumansKnee JointMagnetic Resonance ImagingProspective StudiesAIChatGPTKneeMRI

Identifiers

PMID37950763

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.