ArticleJournal of clinical medicine2023
Comparing Meta-Analyses with ChatGPT in the Evaluation of the Effectiveness and Tolerance of Systemic Therapies in Moderate-to-Severe Plaque Psoriasis.
Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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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
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The emergence of large language models as tools in literature reviews: a large language model-assisted systematic review.Journal of the American Medical Informatics Association : JAMIA · 2025Pooled it
- Evaluating the quality of artificial intelligence responses to psoriasis-related clinical and patient questions: a comparative study of ChatGPT, Gemini, and Microsoft Copilot.Proceedings (Baylor University. Medical Center) · 2026Article
- ChatGPT-4 vs. DeepSeek-V3: a comparative study of response quality, reliability, usefulness, and readability for exercise and rehabilitation strategies in patients with ankylosing spondylitis.Clinical rheumatology · 2026Article
- The Role of ChatGPT in Dermatology Diagnostics.Diagnostics (Basel, Switzerland) · 2025Review
- Application of ChatGPT as a content generation tool in continuing medical education: acne as a test topic.Dermatology reports · 2025Article
- Does ChatGPT help patients access reliable and comprehensive information about psoriasis?Proceedings (Baylor University. Medical Center) · 2025Article
- Large language model enhanced framework for systematic reviews and meta-analyses.BMJ digital health & AI · 2025Article
- Can Artificial Intelligence "Hold" a Dermoscope?-The Evaluation of an Artificial Intelligence Chatbot to Translate the Dermoscopic Language.Diagnostics (Basel, Switzerland) · 2024Article
- Review
- ChatGPT in dermatology: exploring the limited utility amidst the tech hype.Frontiers in medicine · 2023Article
- A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology.Indian dermatology online journalArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMeta-analyses (MAs) and network meta-analyses (NMAs) are high-quality studies for assessing drug efficacy, but they are time-consuming and may be affected by biases. The capacity of artificial intelligence to aggregate huge amounts of information is emerging as particularly interesting for processing the volume of information needed to generate MAs. In this study, we analyzed whether the chatbot ChatGPT is able to summarize information in a useful fashion for providers and patients in a way that matches up with the results of MAs/NMAs.
methodsWe included 16 studies (13 NMAs and 3 MAs) that evaluate biologics (n = 6) and both biologic and systemic treatment (n = 10) for moderate-to-severe psoriasis, published between January 2021 and May 2023.
resultsThe conclusions of the MAs/NMAs were compared to ChatGPT's answers to queries about the molecules evaluated in the selected MAs/NMAs. The reproducibility between the results of ChatGPT and the MAs/NMAs was random regarding drug safety. Regarding efficacy, ChatGPT reached the same conclusion as 5 out of the 16 studies (four out of four studies when three molecules were compared), gave acceptable answers in 7 out of 16 studies, and was inconclusive in 4 out of 16 studies.
conclusionsChatGPT can generate conclusions that are similar to MAs when the efficacy of fewer drugs is compared but is still unable to summarize information in a way that matches up to the results of MAs/NMAs when more than three molecules are compared.
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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.