Evidence map›Paper›PMID 42052412›Full record

ArticleDigital health

Comparative evaluation of ChatGPT-5.0, DeepSeek-R1, and Gemini-2.5 pro in real-world outpatient prescription counseling: A multidimensional analysis.

Quanyuan Huang, Wuchang Zhu, Huizhen Mo, Bingxiu Huang, Zuming Liao, Xiao Lu, Hongliang Zhang

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Quanyuan HuangPharmacy Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.ORCID https://orcid.org/0009-0005-0765-4407
Wuchang ZhuPharmacy Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Huizhen MoPharmacy Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Bingxiu HuangPharmacy Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Zuming LiaoPharmacy Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Xiao LuPharmacy Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Hongliang ZhangPharmacy Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To compare the performance of ChatGPT-5.0, DeepSeek-R1, and Gemini-2.5 Pro in real-world outpatient prescription counseling and evaluate their applicability across clinical contexts. Methods: Fifty authentic prescriptions from four departments were submitted to the three models using standardized Chinese prompts. Responses were independently rated by three associate chief pharmacists across five dimensions-accuracy, relevance, clarity, practicality, and completeness-on a 5-point Likert scale. Rank-based non-parametric tests were applied for overall and subgroup analyses. Results: Significant inter-model differences were observed in most dimensions ( Conclusions: LLMs demonstrate promising yet heterogeneous performance in outpatient medication counseling. DeepSeek and ChatGPT showed superior overall quality, supporting their potential as assistive "AI pharmacists" under professional supervision. However, several limitations should be acknowledged, including a modest sample size, reliance on expert evaluation rather than patient feedback, and context-specific findings that may limit generalizability.

Indexed as

artificial intelligencelarge language modelsmedication counselingoutpatient prescriptionspharmacy services

Identifiers

PMID42052412
PMCPMC13111898

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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.