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ArticleOpen forum infectious diseases2024

Let's Have a Chat: How Well Does an Artificial Intelligence Chatbot Answer Clinical Infectious Diseases Pharmacotherapy Questions?

Wesley D Kufel, Kathleen D Hanrahan, Robert W Seabury, Katie A Parsels, Jason C Gallagher, Conan MacDougall, Elizabeth W Covington, Elias B Chahine, Rachel S Britt, Jeffrey M Steele

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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.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Wesley D KufelSchool of Pharmacy and Pharmaceutical Sciences, Binghamton University, Binghamton, New York, USA.ORCID https://orcid.org/0000-0001-7703-096X
Kathleen D HanrahanState University of New York Upstate University Hospital, Syracuse, New York, USA.ORCID https://orcid.org/0000-0001-9443-0818
Robert W SeaburyState University of New York Upstate University Hospital, Syracuse, New York, USA.
Katie A ParselsState University of New York Upstate University Hospital, Syracuse, New York, USA.ORCID https://orcid.org/0000-0002-6082-0137
Jason C GallagherSchool of Pharmacy, Temple University, Philadelphia, Pennsylvania, USA.ORCID https://orcid.org/0000-0001-8532-0887
Conan MacDougallSchool of Pharmacy, University of California San Francisco, San Francisco, California, USA.ORCID https://orcid.org/0000-0002-8690-5796
Elizabeth W CovingtonHarrison College of Pharmacy, Auburn University, Auburn, Alabama, USA.ORCID https://orcid.org/0000-0003-0082-6008
Elias B ChahineSchool of Pharmacy, Palm Beach Atlantic University Gregory, West Palm Beach, Florida, USA.ORCID https://orcid.org/0000-0003-1775-9497
Rachel S BrittUniversity of Texas Medical Branch at Galveston, Galveston, Texas, USA.ORCID https://orcid.org/0000-0003-2954-6800
Jeffrey M SteeleState University of New York Upstate University Hospital, Syracuse, New York, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

artificial intelligencechatbotChatGPTinfectious diseasespharmacist

Identifiers

PMID39529938
PMCPMC11551448

What OpenQuestion holds

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Registered trials

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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.