Evidence map›Paper›PMID 42327918›Full record

ArticleExploratory research in clinical and social pharmacy2026

Evaluation of large language model responses to patient questions on oral anticoagulant therapy: a comparative expert assessment.

Mohammed Amer Khan, Vaibhav Chaudhary, Mohammed Maazuddin, Ihtisham Sultan, Saamiya Mehnaaz, Biplab Pal

Abstract read
In one paragraph

Article in Exploratory research in clinical and social pharmacy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Mohammed Amer KhanSchool of Pharmaceutical Science, Lovely Professional University, Phagwara, Punjab, India.
Vaibhav ChaudharySchool of Pharmaceutical Science, Lovely Professional University, Phagwara, Punjab, India.
Mohammed MaazuddinSchool of Pharmaceutical Science, Lovely Professional University, Phagwara, Punjab, India.
Ihtisham SultanClinical Pharmacist, Jawaharlal Nehru Technological University Hyderabad, India.
Saamiya MehnaazClinical Pharmacist, Sri Venkateswara College of Pharmacy, Osmania University, Hyderabad, India.
Biplab PalSchool of Pharmaceutical Science, Lovely Professional University, Phagwara, Punjab, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Large language model (LLM)-based chatbots are increasingly used by patients for health information; however, their reliability in high-risk cardiovascular therapies such as oral anticoagulation remains uncertain. This study evaluated the perceived accuracy, clarity, and completeness of LLM-generated responses to common patient queries compared with standard-derived expert responses (SDERs). Methods: A cross-sectional comparative study evaluated responses generated by ChatGPT-4.5, Gemini Pro 2.5, and DeepSeek-V3 to 11 frequently asked questions related to five oral anticoagulants: warfarin, dabigatran, apixaban, rivaroxaban, and edoxaban. Responses were generated using a standardized patient-focused prompt. SDERs were developed by cardiologists and clinical pharmacists using authoritative references. All responses were anonymized and independently assessed by two blinded clinical pharmacists using a five-point Likert scale evaluating accuracy, clarity, and completeness. Interrater reliability was assessed using linearly weighted Cohen's κ, and group comparisons were analyzed using the Friedman test with post hoc adjustments. Results: Interrater reliability ranged from fair to almost perfect (κ = 0.31-0.84). ChatGPT-4.5 achieved the highest mean ratings across all evaluation domains, particularly for completeness. Significant differences were observed among response sources for accuracy, clarity, and completeness ( Conclusion: ChatGPT-4.5 received the highest mean expert ratings for patient education regarding oral anticoagulants. Performance differences were most evident for warfarin-related queries, whereas responses for direct oral anticoagulants were broadly comparable across sources.

Indexed as

ChatGPTDeepSeekGeminiLarge language modelsOral anticoagulantsPatient education

Identifiers

PMID42327918
PMCPMC13279031

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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.