Can large language models provide reliable information on harmful skincare practices? A comparison of ChatGPT-5.2, Gemini 3, and Perplexity Sonar in debunking viral dermatological myths
Year: 2026
Type: article
Abstract: Viral skincare misinformation on social media promotes practices with cutaneous toxicity risks, including irritant contact dermatitis, photosensitization, dyspigmentation, and photocarcinogenic exposure. Free-tier large language models (LLMs) increasingly answer lay skincare queries, yet their debunking performance remains largely unexamined. To compare accuracy, safety-referral behavior, and bibliographic integrity of three widely adopted free-tier LLMs (ChatGPT-5.2, Gemini 3, Perplexity Sonar) responding to patient-voice viral dermatological myths. In this cross-sectional study, twenty myths meeting predefined viral, exposure, dermatotoxicological, and testability criteria were selected by expert consensus from Google Trends and short-form video platforms (July–December 2025). Standardized conversational English prompts were submitted under default free-tier settings. Two blinded board-certified dermatologists independently scored the 60 responses for accuracy (1–5 Likert), safety referral (binary), and bibliographic integrity (verifiable vs hallucinated; peer-review status), resolving disagreements by consensus. Inter-rater agreement was substantial (κ = 0.79). Accuracy did not differ significantly (all medians 4.0; Kruskal–Wallis p = 0.44), whereas safety-referral rates did (ChatGPT 75%, Gemini 90%, Perplexity 55%; p = 0.043; Cramér’s V = 0.32). Gemini hallucinated 62.5% of references; Perplexity reached 95.0% retrieval fidelity, yet only 19.7% of its verifiable citations were peer-reviewed, versus 76.9% (ChatGPT) and 58.3% (Gemini; p < 0.001). All models produced broadly accurate refutations but failed in non-overlapping, dermatotoxicologically meaningful ways. Gemini’s frequent hallucination and Perplexity’s high-fidelity yet largely non-peer-reviewed retrieval illustrate a hallucination paradox: retrieval accuracy is necessary but insufficient for clinical or toxicological reliability. These exploratory findings suggest that patient-facing LLMs warrant cutaneous-safety–aware governance.
Source: Figshare
Author: Orhan Şen
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7225773732
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