ArticleDermatology practical & conceptual2025
Artificial Intelligence in Patient Education for Androgenetic Alopecia: A Comparative Study of ChatGPT, Gemini, and Deepseek R1.
Article in Dermatology practical & conceptual, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Can Artificial Intelligence Support Patient Education in Scabies? A Comparative Analysis of Large Language Model Responses.Healthcare (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Hair Disorders: A Narrative Review.Dermatology and therapy · 2026Review
- Performance evaluation of large language models in bladder cancer patient education Q&A: a cross-sectional study.Frontiers in oncology · 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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionAndrogenetic alopecia (AGA) is a common cause of hair loss worldwide. Accurate patient education may improve treatment adherence and outcomes.
objectiveTo compare the accuracy, readability, and user experience of ChatGPT 4.0, Gemini 1.5 Flash, and Deepseek R1 in answering common patient questions about AGA.
methodsIn February 2025, a cross-sectional study was conducted using 12 frequently asked patient questions on AGA, sourced from online platforms. The questions were submitted to ChatGPT 4.0, Gemini 1.5 Flash, and Deepseek R1. Two dermatologists independently assessed responses using a validated 4-point accuracy scale. Readability was measured with the Flesch-Kincaid Grade Level and Flesch Reading Ease Score. User experience was evaluated based on response speed, presence of visual aids, citation usage, and overall satisfaction. Inter-rater reliability was analyzed via Cohen's kappa, and statistical comparisons were made between models.
resultsChatGPT 4.0 and Gemini 1.5 Flash successfully answered all 12 questions, with most responses rated as "satisfactory with minimal corrections." Deepseek R1 answered only five questions and frequently provided inaccurate content, especially when differentiating between AGA and cicatricial alopecia. It also lacked warnings about potential misinformation. Gemini 1.5 Flash included visual aids and citations, improving interpretability. All models generated responses at a high school reading level. In terms of user experience, ChatGPT 4.0 and Gemini 1.5 Flash outperformed Deepseek R1.
conclusionsChatGPT 4.0 and Gemini 1.5 Flash provided accurate, readable, and user-friendly responses on AGA-related questions, making them promising tools for patient education under physician guidance. Deepseek R1's limitations highlight the need for cautious implementation.
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.