ArticleClinical pharmacology and therapeutics2025
Chat GPT vs. Clinical Decision Support Systems in the Analysis of Drug-Drug Interactions.
Article in Clinical pharmacology and therapeutics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- From Evidence Generation to Decision Making: Completing the Clinical Pharmacology Pathway.Clinical pharmacology and therapeutics · 2026Article
- Navigating uncertainty matters: Evaluating large language models for drug-drug interaction identification.Journal of managed care & specialty pharmacy · 2026Article
- Comparative performance of large language models and Drugs.com versus Lexicomp for antiseizure medication drug-drug interactions: A cross-sectional study with iterative prompting analysis.Exploratory research in clinical and social pharmacy · 2026Article
- Human vs. artificial intelligence: Physicians outperform ChatGPT in real-world pharmacotherapy counselling.British journal of clinical pharmacology · 2026Article
- QT Myopia and Cardiac Safety: Expanding the Aperture of Arrhythmia Assessment in Early Phase Drug Development.Clinical and translational science · 2026Review
- ChatGPT as a Tool for Perioperative Safety: Evaluating Drug Interaction Detection in Anesthetic Regimens.Cureus · 2026Article
- Utility of large language models as information tools for nursing care in gout: a comparative study of DeepSeek and ChatGPT.Frontiers in medicine · 2026Article
- Evaluating a large language model (ChatGPT-5) for detecting potential drug-drug interactions in intensive care: a cross-sectional comparative study with a clinical decision support system.Frontiers in pharmacology · 2026Article
- Drug-drug interaction identification using large language models.medRxiv : the preprint server for health sciences · 2025Article
- Comparative evaluation of artificial intelligence platforms and drug interaction screening databases using real-world patient data.Exploratory research in clinical and social pharmacy · 2025Article
- Challenges of Implementing LLMs in Clinical Practice: Perspectives.Journal of clinical medicine · 2025Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The current standard method for the analysis of potential drug-drug interactions (pDDIs) is time-consuming and includes the use of multiple clinical decision support systems (CDSSs) and the interpretation by healthcare professionals. With the emergence of large language models developed with artificial intelligence, an interesting alternative arose. This retrospective study included 30 patients with polypharmacy, who underwent a pDDI analysis between October 2022 and August 2023, and compared the performance of Chat GPT and established CDSSs (MediQ®, Lexicomp®, Micromedex®) in the analysis of pDDIs. A multidisciplinary team interpreted the obtained results and decided upon clinical relevance and assigned severity grades using three categories: (i) contraindicated, (ii) severe, (iii) moderate. The expert review identified a total of 280 clinically relevant pDDIs (3 contraindications, 13 severe, 264 moderate) using established CDSSs, compared with 80 pDDIs (2 contraindications, 5 severe, 73 moderate) using Chat GPT. Chat GPT almost entirely neglected pDDIs with the risk to QTc prolongation (85 vs. 8), which could also not be sufficiently improved by using a specific prompt. To assess the consistency of the results provided by Chat GPT, we repeated each query and found inconsistent results in 90% of the cases. In contrast, Chat GPT provided acceptable and comprehensible recommendations for specific questions on side effects. The use of Chat GPT for the identification of pDDIs cannot be recommended currently, because clinically relevant pDDIs were not detected, there were obvious errors and results were inconsistent. However, if these limitations are addressed accordingly, it is a promising platform for the future.
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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.