ArticleOpen forum infectious diseases2024
Let's Have a Chat: How Well Does an Artificial Intelligence Chatbot Answer Clinical Infectious Diseases Pharmacotherapy Questions?
Article in Open forum infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers.
What it found
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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
9 citing papers in PubMed.
- The Risks to Patients Associated with Using Artificial Intelligence Tools in Pharmacy Practice: A Scoping Review.Pharmacy (Basel, Switzerland) · 2026Review
- Large Language Models in Infectious Diseases: A Systemic Review.Research square · 2026Article
- Benchmark evaluation of deepseek AI models in antibacterial clinical decision-making for infectious diseases.BMC medical informatics and decision making · 2026Article
- A full educational resource review of grey literature resources for vancomycin dosing tutorials.JAC-antimicrobial resistance · 2026Article
- Large language models as information providers for appropriate antimicrobial use: computational text analysis and expert-rated comparison of ChatGPT, Claude and Gemini.BMJ health & care informatics · 2025Article
- Reply: "Continuing the Chat: How Can We Improve the Performance of an Artificial Intelligence Chatbot in Answering Clinical Infectious Diseases Pharmacotherapy Questions?"Open forum infectious diseases · 2025Article
- Continuing the Chat: How Can We Improve the Performance of an Artificial Intelligence Chatbot in Answering Clinical Infectious Diseases Pharmacotherapy Questions?Open forum infectious diseases · 2025Article
- Evaluating chain-of-thought prompting in a GPT chatbot for BCID2 interpretation and stewardship: how does AI compare to human experts?Antimicrobial stewardship & healthcare epidemiology : ASHE · 2025Article
- Can we rely on artificial intelligence to guide antimicrobial therapy? A systematic literature review.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: It is unknown whether ChatGPT provides quality responses to infectious diseases (ID) pharmacotherapy questions. This study surveyed ID pharmacist subject matter experts (SMEs) to assess the quality of ChatGPT version 3.5 (GPT-3.5) responses. Methods: The primary outcome was the percentage of GPT-3.5 responses considered useful by SME rating. Secondary outcomes were SMEs' ratings of correctness, completeness, and safety. Rating definitions were based on literature review. One hundred ID pharmacotherapy questions were entered into GPT-3.5 without custom instructions or additional prompts, and responses were recorded. A 0-10 rating scale for correctness, completeness, and safety was developed and validated for interrater reliability. Continuous and categorical variables were assessed for interrater reliability via average measures intraclass correlation coefficient and Fleiss multirater kappa, respectively. SMEs' responses were compared by the Kruskal-Wallis test and chi-square test for continuous and categorical variables. Results: SMEs considered 41.8% of responses useful. Median (IQR) ratings for correctness, completeness, and safety were 7 (4-9), 5 (3-8), and 8 (4-10), respectively. The Fleiss multirater kappa for usefulness was 0.379 (95% CI, .317-.441) indicating fair agreement, and intraclass correlation coefficients were 0.820 (95% CI, .758-.870), 0.745 (95% CI, .656-.816), and 0.833 (95% CI, .775-.880) for correctness, completeness, and safety, indicating at least substantial agreement. No significant difference was observed among SME responses for percentage of responses considered useful. Conclusions: Fewer than 50% of GPT-3.5 responses were considered useful by SMEs. Responses were mostly considered correct and safe but were often incomplete, suggesting that GPT-3.5 responses may not replace an ID pharmacist's responses.
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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.