PharmacyGPT: The AI Pharmacist
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
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| _version_ | 1866914962985713664 |
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| author | Liu, Zhengliang Wu, Zihao Hu, Mengxuan Zhao, Bokai Zhao, Lin Zhang, Tianyi Dai, Haixing Chen, Xianyan Shen, Ye Li, Sheng Li, Quanzheng Li, Xiang Murray, Brian Liu, Tianming Sikora, Andrea |
| author_facet | Liu, Zhengliang Wu, Zihao Hu, Mengxuan Zhao, Bokai Zhao, Lin Zhang, Tianyi Dai, Haixing Chen, Xianyan Shen, Ye Li, Sheng Li, Quanzheng Li, Xiang Murray, Brian Liu, Tianming Sikora, Andrea |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_10432 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | PharmacyGPT: The AI Pharmacist Liu, Zhengliang Wu, Zihao Hu, Mengxuan Zhao, Bokai Zhao, Lin Zhang, Tianyi Dai, Haixing Chen, Xianyan Shen, Ye Li, Sheng Li, Quanzheng Li, Xiang Murray, Brian Liu, Tianming Sikora, Andrea Computation and Language 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. |
| title | PharmacyGPT: The AI Pharmacist |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2307.10432 |