Comparing Meta-Analyses with ChatGPT in the Evaluation of the Effectiveness and Tolerance of Systemic Therapies in Moderate-to-Severe Plaque Psoriasis

Background: Meta-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. Methods: We 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. Results: The 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. Conclusions: ChatGPT 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.
SCOPUS: ar.j
info:eu-repo/semantics/published