ArticleDigital health
Assessing the accuracy and readability of large language models in answering Turkish-language patient-generated queries on radiological contrast agents.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To assess the clinical accuracy, structural readability, and Ensuring Quality Information for Patients (EQIP-36) quality of Large Language Model (LLM) responses to real patient queries regarding radiological contrast agents and to define their safe boundaries in practice. Methods: The 30 most popular Turkish patient questions reflecting fears and risk perceptions about contrast agents were identified via Google Trends and AlsoAsked.com. These queries were presented to ChatGPT-5.4, Gemini 3, and Claude 4.6 Sonnet using a zero-shot learning approach. Responses were evaluated by experts for clinical accuracy against the 2024/2025 American College of Radiology (ACR) Manual on Contrast Media. Information quality and readability were measured using the expanded EQIP-36 scale and the Ateşman and Bezirci-Yılmaz indices. Results: Claude 4.6 Sonnet demonstrated the highest clinical accuracy and high compliance with guidelines (>90%). Gemini 3 exhibited a more conservative stance, occasionally resulting in over-triage by exaggerating risks. While all models scored exceptionally well in the Content and Structure dimensions of the EQIP-36, generating readable texts, they universally failed in the Identification dimension by omitting author names, update dates, or bibliographic sources. Readability indices indicated that comprehending the texts required an average of 10.5 to 11.6 years of formal education. Conclusion: LLMs generate highly readable and subjectively reassuring Turkish-language texts in response to patient queries regarding radiological contrast agents. However, due to domain-specific structural gaps regarding quantitative thresholds and lack of citations, they should be positioned as hybrid communication tools subject to mandatory review by specialist physicians rather than standalone medical advisors.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.