Evidence map›Paper›PMID 39931624›Full record

ArticleCureus2025

Is ChatGPT a Reliable Tool for Explaining Medical Terms?

Ayse Dilara Oztermeli

Abstract read
In one paragraph

Article in Cureus, 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

1 author.

Ayse Dilara OztermeliEmergency Medicine, Derince State Hospital, Kocaeli, TUR.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background The increasing reliance on the internet for health-related information has driven interest in artificial intelligence (AI) applications in healthcare. ChatGPT has demonstrated strong performance in medical exams, raising questions about its potential use in patient education. However, no prior study has evaluated the reliability of ChatGPT in explaining medical terms. This study investigates whether ChatGPT-4 is a reliable tool for translating frequently used medical terms into language that patients can understand. Methodology A total of 105 frequently used medical terms were selected from the University of San Diego's medical terminology list. Four groups - general practitioners, resident physicians, specialist physicians, and ChatGPT-4 - were tasked with defining these terms. Responses were classified as correct or incorrect. Statistical analyses, including chi-square and post-hoc tests, were conducted to compare accuracy rates across groups. Results ChatGPT-4 achieved a 100% accuracy rate, outperforming specialist physicians (98.1%), resident physicians (93.3%), and general practitioners (84.8%). The differences in accuracy rates between groups were statistically significant (χ²=25.99, p<0.00001). Post-hoc analyses confirmed significant pairwise differences, such as ChatGPT-4 vs. specialist physicians (p<0.001) and specialist physicians vs. resident physicians (p=0.02). Conclusions ChatGPT-4 demonstrated superior reliability in translating medical terms into understandable language, surpassing even highly experienced physicians. These findings suggest that ChatGPT could be a valuable auxiliary tool for improving patient comprehension of medical terminology. Nonetheless, the importance of consulting healthcare professionals for clinical decision-making remains crucial.

Indexed as

artificial intelligencechatgptclinical decision supportmedical terminologypatient education

Identifiers

PMID39931624
PMCPMC11808164

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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.