ArticleJMIR cancer2026
ChatGPT Versus DeepSeek for Breast Cancer Information Retrieval: Quantitative Comparative Study.
Article in JMIR cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Beyond accuracy: evaluating the reliability of large language models for medical assessment.Frontiers in artificial intelligence · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) is increasingly used to generate medical content, yet its performance in delivering clinically relevant and reliable information remains underexplored, especially in complex areas such as breast cancer. Objective: This study aimed to compare ChatGPT-4.0 and DeepSeek-V3 in generating breast cancer information, focusing on readability, content quality, and citation reliability. Methods: On the basis of publicly available patient education materials, 10 frequently asked questions were selected. Each model generated 60 responses. Three expert reviewers rated each response using a 7-point Likert scale across 5 dimensions (ie, accuracy, completeness, clarity, depth and insight, and alignment with expert answers). Readability was assessed using Flesch-Kincaid Grade Level scores. Information reliability was evaluated through interrater agreement metrics, including Cohen κ and Fleiss κ. Paired t tests were used for statistical comparisons. Results: AI models produced significantly more readable content than expert references (mean Flesch-Kincaid Grade Level difference -2.60; P<.001). ChatGPT-4.0 responses were more stylistically consistent with a median Flesch-Kincaid Grade Level score of 10.66 (IQR 0.98), whereas DeepSeek-V3 showed greater variability with a median Flesch-Kincaid Grade Level score of 10.17 (IQR 1.41). Content quality scores were DeepSeek-V3 achieving a higher mean score than ChatGPT-4.0 (6.22 [SD 0.43] vs 6.01 [SD 0.49]). In the multiresponse analysis, DeepSeek-V3 demonstrated a statistically significant advantage in accuracy (P=.041), while differences across other criteria were not statistically significant (P>.05). Human raters showed almost perfect agreement when judging source reliability (Fleiss κ=0.842 for ChatGPT's citations and 0.935 for DeepSeek's citations). Agreement between each model's citation reliability scores and the expert majority was substantial for ChatGPT (Cohen κ=0.665) and higher for DeepSeek (Cohen κ=0.782). Conclusions: Both models generated readable and clinically relevant content with comparable overall performance. ChatGPT provided more consistent readability, while DeepSeek offered more diverse references with stronger alignment to expert ratings. Continued evaluation and quality assurance are essential for the responsible clinical use of AI-generated content.
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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.