Large language models in pharmacist-led direct patient care: A rapid review of published evaluations

Efficient healthcare with large language models: optimizing clinical workflow and enhancing patient care

10.1093/jamia/ocad258 · doi-reference

Ensuring useful adoption of generative artificial intelligence in healthcare

10.1093/jamia/ocae043 · doi-reference

Navigating the European Union Artificial Intelligence Act for healthcare

10.1038/s41746-024-01213-6 · doi-reference

Evaluating the clinical benefits of LLMs

10.1038/s41591-024-03181-6 · doi-reference

Prompt engineering on leveraging large language models in generating response to InBasket messages

10.1093/jamia/ocae172 · doi-reference

ChatGPT in pharmacy practice: a cross-sectional exploration of Jordanian pharmacists' perception, practice, and concerns

10.1186/s40545-023-00624-2 · doi-reference

Densing law of LLMs

10.1038/s42256-025-01137-0 · doi-reference

Medical students' use of large language models: a national survey

10.1016/j.ijmedinf.2026.106589 · doi-reference

Retrieval augmented generation for large language models in healthcare: a systematic review

10.1371/journal.pdig.0000877 · doi-reference

The TRIPOD-LLM reporting guideline for studies using large language models

10.1038/s41591-024-03425-5 · doi-reference

A framework for human evaluation of large language models in healthcare derived from literature review

10.1038/s41746-024-01258-7 · doi-reference

Holistic evaluation of language models

10.1111/nyas.15007 · doi-reference

Between the booms: AI in Winter

10.1145/3688379 · doi-reference

Benchmark evaluation of deepseek AI models in antibacterial clinical decision-making for infectious diseases

10.1186/s12911-026-03364-w · doi-reference

Performance validation of large language models for drug-drug interaction consultations: potential pharmacist assistant in outpatient pharmaceutical services

10.1016/j.japh.2026.103077 · doi-reference

Assessment of artificial intelligence (AI)‐powered self‐care recommendations for management of minor ailments: a comparative analysis

10.1002/phar.70089 · doi-reference

Conducting medication reviews: a comparative study between ChatGPT‐4 and healthcare professionals

10.1111/jgs.70415 · doi-reference

Assessing the accuracy of 4 generative AI tools to guide dosing of long-acting injectable antipsychotics

10.1093/ajhp/zxag089 · doi-reference

Assessing ChatGPT's capability in understanding and reporting antiretroviral therapy drug–drug interactions (ACCURATE-DDI)

10.1097/qad.0000000000004522 · doi-reference

Improving medication error classification using a reasoning large language model

10.1093/jamiaopen/ooag004 · doi-reference

Artificial intelligence for drug information: evaluating its use in real-world pharmacy practice

10.1093/ajhp/zxaf350 · doi-reference

Real-world evaluation of large language models in detecting drug-related problems: a clinical pharmacist–AI concordance study in hematology care

10.1177/10781552261418957 · doi-reference

Comparative accuracy, stability, and correctability of large language models in otolaryngology and pharmacovigilance

10.1002/ohn.70070 · doi-reference

ChatGPT-5 for drug–drug interaction detection in the intensive care unit: a real-world cohort study on large language model advances and implications for clinical pharmacists

10.1016/j.japh.2026.103019 · doi-reference

Evaluating a generative artificial intelligence accuracy in providing medication instructions from smartphone images

10.1016/j.japh.2024.102284 · doi-reference

Analysis of responses from artificial intelligence programs to medication-related questions derived from critical care guidelines

10.1093/ajhp/zxaf075 · doi-reference

A comparison of drug information question responses by a drug information center and by ChatGPT

10.1093/ajhp/zxae316 · doi-reference

Assessing GPT‐4’s accuracy in answering clinical pharmacological questions on pain therapy

10.1002/bcp.70036 · doi-reference

Investigating the capabilities of advanced large language models in generating patient instructions and patient educational material

10.1136/ejhpharm-2024-004245 · doi-reference

Identifying deprescribing opportunities with large language models in older adults: retrospective cohort study

10.2196/69504 · doi-reference

Can large language models detect drug–drug interactions leading to adverse drug reactions?

10.1177/20420986251339358 · doi-reference

Effectiveness of ChatGPT, Google Gemini, and Microsoft copilot in answering Thai drug information queries: cross-sectional study

10.2196/79751 · doi-reference

Evaluation of the accuracy and reliability of responses generated by artificial intelligence related to clinical pharmacology

10.3390/jcm14217563 · doi-reference

Comparative analysis of generative artificial intelligence systems in solving clinical pharmacy problems: mixed methods study

10.2196/76128 · doi-reference

Risk stratification of potential drug interactions involving common over-the-counter medications and herbal supplements by a large language model

10.1016/j.japh.2024.102304 · doi-reference

The role of large language models in self-care: a study and benchmark on medicines and supplement guidance accuracy

10.1007/s11096-024-01839-2 · doi-reference

Accuracy and safety of ChatGPT-3.5 in assessing over-the-counter medication use during pregnancy: a descriptive comparative study

10.3390/pharmacy13040104 · doi-reference

Large language models management of complex medication regimens: a case-based evaluation

10.3389/fphar.2025.1514445 · doi-reference

Accuracy and teachability of artificial intelligence chatbots in solving pharmaceutical calculations: a descriptive study

10.1007/s11096-025-01947-7 · doi-reference

Capabilities of large language models in detecting and managing drug interactions during medication reviews: potential implications as A digital assistant for pragmatic pharmacy practice in Thailand

10.1002/jac5.70115 · doi-reference