Evidence map›Paper›PMID 39359001›Full record

ArticleBritish journal of clinical pharmacology2024

Evaluating the capability of ChatGPT in predicting drug-drug interactions: Real-world evidence using hospitalized patient data.

Ramya Padmavathy Radha Krishnan, Euniss Hinyo Hung, Megan Ashford, Clark Ethan Edillo, Charlise Gardner, Hector Blake Hatrick, Byungjun Kim, Angel Wing Yan Lai, Xinran Li, Yvonne Xinyi Zhao and 1 more

Abstract read
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In one paragraph

Article in British journal of clinical pharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
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  5. Article
  6. Drug-drug interaction identification using large language models.medRxiv : the preprint server for health sciences · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Ramya Padmavathy Radha KrishnanFaculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-3495-5919
Euniss Hinyo HungFaculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia.
Megan AshfordFaculty of Science, University of Sydney, Sydney, New South Wales, Australia.
Clark Ethan EdilloFaculty of Science, University of Sydney, Sydney, New South Wales, Australia.
Charlise GardnerFaculty of Science, University of Sydney, Sydney, New South Wales, Australia.
Hector Blake HatrickFaculty of Science, University of Sydney, Sydney, New South Wales, Australia.
Byungjun KimFaculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia.
Angel Wing Yan LaiFaculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia.
Xinran LiFaculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia.
Yvonne Xinyi ZhaoFaculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia.
Jacques Eugene RaubenheimerFaculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Drug-drug interactions (DDIs) present a significant health burden, compounded by clinician time constraints and poor patient health literacy. We assessed the ability of ChatGPT (generative artificial intelligence-based large language model) to predict DDIs in a real-world setting. Demographics, diagnoses and prescribed medicines for 120 hospitalized patients were input through three standardized prompts to ChatGPT version 3.5 and compared against pharmacist DDI evaluation to estimate diagnostic accuracy. Area under receiver operating characteristic and inter-rater reliability (Cohen's and Fleiss' kappa coefficients) were calculated. ChatGPT's responses differed based on prompt wording style, with higher sensitivity for prompts mentioning 'drug interaction'. Confusion matrices displayed low true positive and high true negative rates, and there was minimal agreement between ChatGPT and pharmacists (Cohen's kappa values 0.077-0.143). Low sensitivity values suggest a lack of success in identifying DDIs by ChatGPT, and further development is required before it can reliably assess potential DDIs in real-world scenarios.

Indexed as

Drug InteractionsAdultAgedAged, 80 and overArtificial IntelligenceFemaleHospitalizationHumansMaleMiddle AgedPharmacistsReproducibility of ResultsROC Curveartificial intelligence (AI)ChatGPTdrug–drug interaction (DDI)large language model (LLM)patient health literacy

Identifiers

PMID39359001
PMCPMC11602951

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.