Evidence map›Paper›PMID 40690168›Full record

ArticleAnnals of surgical oncology2025

The Use of ChatGPT-4.0 to Simplify Breast Pathology Reports: A Study on Readability and Accuracy.

Samhita Bheemireddy, Sarah E Leslie, Jakob A Durden, George Burnet, Zain Aryanpour, Ashlyn Fong, Madeline G Higgins, Samantha Greenseid, Lauren McLemore, Gande Li and 3 more

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Article in Annals of surgical oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

13 authors.

Samhita BheemireddyAlbany Medical College, Albany Medical Center, Albany, NY, USA. bheemis1@amc.edu.ORCID http://orcid.org/0009-0003-5266-1572
Sarah E LeslieAdult and Child Center for Outcomes Research and Delivery Science (ACCORDS), University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Jakob A DurdenDepartment of Surgery, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
George BurnetDepartment of Surgery, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Zain AryanpourDepartment of Surgery, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Ashlyn FongDepartment of Pathology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Madeline G HigginsDepartment of Surgery, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Samantha GreenseidDepartment of Surgical Oncology, University of Miami, Miami, FL, USA.
Lauren McLemoreDepartment of Pathology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Gande LiDepartment of Radiology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Randy MilesDepartment of Radiology, Denver Health and Hospital Authority, Denver, CO, USA.
Nancy TaftDepartment of General Surgery, Denver Health and Hospital Authority, Denver, CO, USA.
Sarah TevisDepartment of Surgery, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

Funding

Cancer Center, University of Colorado P30CA046934Doris Duke Charitable Foundation 2020141
6 · The paper itself

Abstract

backgroundPatients have immediate access to their diagnostic reports but these reports exceed the recommended reading level for patient-facing materials. Generative artificial intelligence may be a tool for improving patient comprehension of health information. This study assessed the readability and accuracy of ChatGPT-simplified breast pathology reports.

methodsTen de-identified patient breast pathology reports were simplified by ChatGPT-4.0 using three different prompts. Prompt 1 requested simplification, Prompt 2 added a 6th-grade-level specification, and Prompt 3 requested essential information. The Flesch-Kincaid Reading Level (FKRL) and Flesch Reading Ease Score (FRES) were utilized to quantify readability and ease of reading, respectively. Five physicians used a four-point scale to assess factual correctness, relevancy, and fabrications to determine overall accuracy. Mean scores and standard deviations for FKRL, FRES, and accuracy scores were compared using analysis of variance (ANOVA) and t-tests.

resultsPrompt 2 demonstrated a reduction in FKRL (p < 0.001) and an increase in FRES (p < 0.001), demonstrating improved readability and ease of reading. ChatGPT-simplified reports received an overall accuracy score of 3.59/4 (standard deviation [SD] ± 0.17). The scores by rubric category were 3.62 (SD ± 0.31) for factual correctness (4 = completely correct), 3.27 (SD ± 0.44) for relevancy (4 = completely relevant), and 3.89 (SD ± 0.11) for fabricated information (4 = no fabricated information).

conclusionsChatGPT simplified breast pathology reports to the reading level recommended for patient-facing materials when given a grade-level specification while mostly maintaining accuracy. To minimize the risk of medically inaccurate and/or misleading information, ChatGPT-simplified reports should be reviewed before dissemination.

Indexed as

Artificial IntelligenceBreast NeoplasmsComprehensionHealth LiteracyFemaleGenerative Artificial IntelligenceHumansReadingAccuracyBreast pathology reportsChatGPTHealth literacyReadability

Identifiers

PMID40690168

What Socratic 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 Socratic graph.