Evidence map›Paper›PMID 41509866›Full record

ArticleDigital health

Retrospective modeling study of ChatGPT (GPT 4) warfarin dose adjustment in patients with INRs outside the therapeutic range.

Hüseyin Tezcan, Abdullah Tunçez, Yasin Özen, Muhammed Ulvi Yalçın, Kadri Murat Gürses

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Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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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

5 authors.

Hüseyin TezcanDepartment of Cardiology, Konya Selcuk University Medical Faculty Hospital, Konya, Turkey.ORCID https://orcid.org/0000-0002-8140-874X
Abdullah TunçezDepartment of Cardiology, Konya Selcuk University Medical Faculty Hospital, Konya, Turkey.
Yasin ÖzenDepartment of Cardiology, Konya Selcuk University Medical Faculty Hospital, Konya, Turkey.
Muhammed Ulvi YalçınDepartment of Cardiology, Konya Selcuk University Medical Faculty Hospital, Konya, Turkey.
Kadri Murat GürsesDepartment of Cardiology, Konya Selcuk University Medical Faculty Hospital, Konya, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This retrospective, modeled study evaluates the accuracy of ChatGPT (GPT-4)-based warfarin dose adjustments compared to clinician recommendations at the Cardiology Clinic of Konya City Hospital, focusing on patients with international normalized ratio (INR) values outside the therapeutic range (2-3). We hypothesized that ChatGPT could provide reliable, consistent dose guidance. Methods: We reviewed the records of warfarin-treated patients from 1 June 2022 through 24 November 2024. Clinical data used by physicians (e.g. baseline INR, warfarin indication, comorbidities, and current dose) were provided to ChatGPT to generate hypothetical weekly dose recommendations. ChatGPT's impact on INR normalization was modeled using standard dose-response assumptions and compared with actual outcomes under physician-guided therapy. Results: A total of 180 patients met the inclusion criteria. ChatGPT's recommended doses were within ±1 mg/week of physician prescriptions in 74% of cases and within ±2 mg/week in 84%. The mean physician dose was 28.0 ± 6.1 mg/week versus ChatGPT's 27.5 ± 5.9 mg/week ( Conclusion: In this retrospective, exploratory analysis with modeled outcomes, ChatGPT's weekly dose suggestions showed high concordance with clinician dosing. These findings are hypothesis-generating and do not establish clinical efficacy or safety; prospective, physician-supervised trials-potentially integrated with home INR monitoring-are required for validation.

Indexed as

anticoagulationartificial intelligenceChatGPTdosingINR monitoringWarfarin

Identifiers

PMID41509866
PMCPMC12775298

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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.