ArticleJMIR medical informatics2025
Comparative Analysis of Generative Artificial Intelligence Systems in Solving Clinical Pharmacy Problems: Mixed Methods Study.
Article in JMIR medical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI in pharmacy education: a comparative visualization analysis of global and Chinese research trends.Frontiers in medicine · 2026Pooled it
- Article
- Open-Source Large Language Models Distilled DeepSeek-R1 Pose Challenges for On-Premises Clinical Deployment in Medical Diagnosis: A Comparative Study of Performance.Journal of medical systems · 2026Article
- Performance comparison of large language models for medication counseling in people living with HIV.Frontiers in public health · 2026Article
- Guiding student use of generative AI in undergraduate pharmacology: a narrative review and proposed source-checking process framework.Frontiers in medicine · 2026Review
- Integrating large language models into medical undergraduate laboratory course to enhance bioethical competence: a quasi-experimental study.Frontiers in medicine · 2025Article
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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
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Abstract
Background: Generative artificial intelligence (AI) systems are increasingly deployed in clinical pharmacy; yet, systematic evaluation of their efficacy, limitations, and risks across diverse practice scenarios remains limited. Objective: This study aims to quantitatively evaluate and compare the performance of 8 mainstream generative AI systems across 4 core clinical pharmacy scenarios-medication consultation, medication education, prescription review, and case analysis with pharmaceutical care-using a multidimensional framework. Methods: Forty-eight clinically validated questions were selected via stratified sampling from real-world sources (eg, hospital consultations, clinical case banks, and national pharmacist training databases). Three researchers simultaneously tested 8 different generative AI systems (ERNIE Bot, Doubao, Kimi, Qwen, GPT-4o, Gemini-1.5-Pro, Claude-3.5-Sonnet, and DeepSeek-R1) using standardized prompts within a single day (February 20, 2025). A double-blind scoring design was used, with 6 experienced clinical pharmacists (≥5 years experience) evaluating the AI responses across 6 dimensions: accuracy, rigor, applicability, logical coherence, conciseness, and universality, scored 0-10 per predefined criteria (eg, -3 for inaccuracy and -2 for incomplete rigor). Statistical analysis used one-way ANOVA with Tukey Honestly Significant Difference (HSD) post hoc testing and intraclass correlation coefficients (ICC) for interrater reliability (2-way random model). Qualitative thematic analysis identified recurrent errors and limitations. Results: DeepSeek-R1 (DeepSeek) achieved the highest overall performance (mean composite score: medication consultation 9.4, SD 1.0; case analysis 9.3, SD 1.0), significantly outperforming others in complex tasks (P<.05). Critical limitations were observed across models, including high-risk decision errors-75% omitted critical contraindications (eg, ethambutol in optic neuritis) and a lack of localization-90% erroneously recommended macrolides for drug-resistant Mycoplasma pneumoniae (China's high-resistance setting), while only DeepSeek-R1 aligned with updated American Academy of Pediatrics (AAP) guidelines for pediatric doxycycline. Complex reasoning deficits: only Claude-3.5-Sonnet detected a gender-diagnosis contradiction (prostatic hyperplasia in female); no model identified diazepam's 7-day prescription limit. Interrater consistency was lowest for conciseness in case analysis (ICC=0.70), reflecting evaluator disagreement on complex outputs. ERNIE Bot (Baidu) consistently underperformed (case analysis: 6.8, SD 1.5; P<.001 vs DeepSeek-R1). Conclusions: While generative AI shows promise as a pharmacist assistance tool, significant limitations-including high-risk errors (eg, contraindication omissions), inadequate localization, and complex reasoning gaps-preclude autonomous clinical decision-making. Performance stratification highlights DeepSeek-R1's current advantage, but all systems require optimization in dynamic knowledge updating, complex scenario reasoning, and output interpretability. Future deployment must prioritize human oversight (human-AI co-review), ethical safeguards, and continuous evaluation frameworks.
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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 Socratic graph.