PharmacyGPT: The AI Pharmacist
- URL: http://arxiv.org/abs/2307.10432v2
- Date: Fri, 21 Jul 2023 02:22:14 GMT
- Title: PharmacyGPT: The AI Pharmacist
- Authors: Zhengliang Liu, Zihao Wu, Mengxuan Hu, Bokai Zhao, Lin Zhao, Tianyi
Zhang, Haixing Dai, Xianyan Chen, Ye Shen, Sheng Li, Brian Murray, Tianming
Liu, Andrea Sikora
- Abstract summary: We introduce PharmacyGPT, a framework to assess the capabilities of large language models (LLMs) in emulating the role of clinical pharmacists.
We conduct our investigation using real data acquired from the intensive care unit (ICU) at the University of North Carolina Chapel Hill (UNC) Hospital.
- Score: 13.05835911291277
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this study, we introduce PharmacyGPT, a novel framework to assess the
capabilities of large language models (LLMs) such as ChatGPT and GPT-4 in
emulating the role of clinical pharmacists. Our methodology encompasses the
utilization of LLMs to generate comprehensible patient clusters, formulate
medication plans, and forecast patient outcomes. We conduct our investigation
using real data acquired from the intensive care unit (ICU) at the University
of North Carolina Chapel Hill (UNC) Hospital. Our analysis offers valuable
insights into the potential applications and limitations of LLMs in the field
of clinical pharmacy, with implications for both patient care and the
development of future AI-driven healthcare solutions. By evaluating the
performance of PharmacyGPT, we aim to contribute to the ongoing discourse
surrounding the integration of artificial intelligence in healthcare settings,
ultimately promoting the responsible and efficacious use of such technologies.
Related papers
- Explainable Biomedical Hypothesis Generation via Retrieval Augmented Generation enabled Large Language Models [46.05020842978823]
Large Language Models (LLMs) have emerged as powerful tools to navigate this complex data landscape.
RAGGED is a comprehensive workflow designed to support investigators with knowledge integration and hypothesis generation.
arXiv Detail & Related papers (2024-07-17T07:44:18Z) - Dr-LLaVA: Visual Instruction Tuning with Symbolic Clinical Grounding [53.629132242389716]
Vision-Language Models (VLM) can support clinicians by analyzing medical images and engaging in natural language interactions.
VLMs often exhibit "hallucinogenic" behavior, generating textual outputs not grounded in contextual multimodal information.
We propose a new alignment algorithm that uses symbolic representations of clinical reasoning to ground VLMs in medical knowledge.
arXiv Detail & Related papers (2024-05-29T23:19:28Z) - Automatic Interactive Evaluation for Large Language Models with State Aware Patient Simulator [21.60103376506254]
Large Language Models (LLMs) have demonstrated remarkable proficiency in human interactions.
This paper introduces the Automated Interactive Evaluation (AIE) framework and the State-Aware Patient Simulator (SAPS)
AIE and SAPS provide a dynamic, realistic platform for assessing LLMs through multi-turn doctor-patient simulations.
arXiv Detail & Related papers (2024-03-13T13:04:58Z) - AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator [69.51568871044454]
We introduce textbfAI Hospital, a framework simulating dynamic medical interactions between emphDoctor as player and NPCs.
This setup allows for realistic assessments of LLMs in clinical scenarios.
We develop the Multi-View Medical Evaluation benchmark, utilizing high-quality Chinese medical records and NPCs.
arXiv Detail & Related papers (2024-02-15T06:46:48Z) - Large Language Model Distilling Medication Recommendation Model [61.89754499292561]
We harness the powerful semantic comprehension and input-agnostic characteristics of Large Language Models (LLMs)
Our research aims to transform existing medication recommendation methodologies using LLMs.
To mitigate this, we have developed a feature-level knowledge distillation technique, which transfers the LLM's proficiency to a more compact model.
arXiv Detail & Related papers (2024-02-05T08:25:22Z) - Generative Large Language Models are autonomous practitioners of
evidence-based medicine [27.229179922424063]
Evidence-based medicine (EBM) is fundamental to modern clinical practice, requiring clinicians to continually update their knowledge and apply the best clinical evidence in patient care.
The practice of EBM faces challenges due to rapid advancements in medical research, leading to information overload for clinicians.
The integration of artificial intelligence (AI), specifically Generative Large Language Models (LLMs), offers a promising solution towards managing this complexity.
arXiv Detail & Related papers (2024-01-05T15:09:57Z) - ABiMed: An intelligent and visual clinical decision support system for
medication reviews and polypharmacy management [3.843569766201585]
The aim of ABiMed is to design an innovative clinical decision support system for medication reviews and polypharmacy management.
ABiMed associates several approaches: guidelines implementation, but the automatic extraction of patient data from the GP's electronic health record and its transfer to the pharmacist, and the visual presentation of contextualized drug knowledge using visual analytics.
arXiv Detail & Related papers (2023-12-13T11:06:45Z) - Large Language Models Illuminate a Progressive Pathway to Artificial
Healthcare Assistant: A Review [16.008511195589925]
Large language models (LLMs) have shown promising capabilities in mimicking human-level language comprehension and reasoning.
This paper provides a comprehensive review on the applications and implications of LLMs in medicine.
arXiv Detail & Related papers (2023-11-03T13:51:36Z) - SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction
and Drug Design [64.69434941796904]
We propose a novel setting and models for in-context drug synergy learning.
We are given a small "personalized dataset" of 10-20 drug synergy relationships in the context of specific cancer cell targets.
Our goal is to predict additional drug synergy relationships in that context.
arXiv Detail & Related papers (2023-06-19T17:03:46Z) - Large Language Models for Healthcare Data Augmentation: An Example on
Patient-Trial Matching [49.78442796596806]
We propose an innovative privacy-aware data augmentation approach for patient-trial matching (LLM-PTM)
Our experiments demonstrate a 7.32% average improvement in performance using the proposed LLM-PTM method, and the generalizability to new data is improved by 12.12%.
arXiv Detail & Related papers (2023-03-24T03:14:00Z) - Large Language Models for Biomedical Knowledge Graph Construction:
Information extraction from EMR notes [0.0]
We propose an end-to-end machine learning solution based on large language models (LLMs)
The entities used in the KG construction process are diseases, factors, treatments, as well as manifestations that coexist with the patient while experiencing the disease.
The application of the proposed methodology is demonstrated on age-related macular degeneration.
arXiv Detail & Related papers (2023-01-29T15:52:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.