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Ong JCL, Chen MH, Ng N, Elangovan K, Tan NYT, Jin L, Xie Q, Ting DSW, Rodriguez-Monguio R, Bates DW, Liu N. A scoping review on generative AI and large language models in mitigating medication related harm. NPJ Digit Med 2025; 8:182. [PMID: 40155703 PMCID: PMC11953325 DOI: 10.1038/s41746-025-01565-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2024] [Accepted: 03/06/2025] [Indexed: 04/01/2025] Open
Abstract
Medication-related harm has a significant impact on global healthcare costs and patient outcomes. Generative artificial intelligence (GenAI) and large language models (LLM) have emerged as a promising tool in mitigating risks of medication-related harm. This review evaluates the scope and effectiveness of GenAI and LLM in reducing medication-related harm. We screened 4 databases for literature published from 1st January 2012 to 15th October 2024. A total of 3988 articles were identified, and 30 met the criteria for inclusion into the final review. Generative AI and LLMs were applied in three key applications: drug-drug interaction identification and prediction, clinical decision support, and pharmacovigilance. While the performance and utility of these models varied, they generally showed promise in early identification, classification of adverse drug events, and supporting decision-making for medication management. However, no studies tested these models prospectively, suggesting a need for further investigation into integration and real-world application.
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Affiliation(s)
- Jasmine Chiat Ling Ong
- Division of Pharmacy, Singapore General Hospital, Singapore, Singapore
- Department of Pharmacy, University of California, San Francisco, CA, USA
- Duke-NUS Medical School, Singapore, Singapore
| | | | - Ning Ng
- Artificial Intelligence Office, Singapore Health Services, Singapore, Singapore
| | - Kabilan Elangovan
- Artificial Intelligence Office, Singapore Health Services, Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | | | - Liyuan Jin
- Duke-NUS Medical School, Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Qihuang Xie
- School of Pharmacy, National University of Singapore, Singapore, Singapore
| | - Daniel Shu Wei Ting
- Artificial Intelligence Office, Singapore Health Services, Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Byers Eye Institute, Stanford University, California, CA, USA
| | - Rosa Rodriguez-Monguio
- Department of Clinical Pharmacy, School of Pharmacy, University of California, San Francisco, CA, USA
- Medication Outcomes Center, University of California, San Francisco, CA, USA
| | - David W Bates
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Nan Liu
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore.
- NUS AI Institute, National University of Singapore, Singapore, Singapore.
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Cui Z, Qi C, Zhou T, Yu Y, Wang Y, Zhang Z, Zhang Y, Wang W, Liu Y. Artificial intelligence and food flavor: How AI models are shaping the future and revolutionary technologies for flavor food development. Compr Rev Food Sci Food Saf 2025; 24:e70068. [PMID: 39783879 DOI: 10.1111/1541-4337.70068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2024] [Revised: 10/16/2024] [Accepted: 11/04/2024] [Indexed: 01/12/2025]
Abstract
The food flavor science, traditionally reliant on experimental methods, is now entering a promising era with the help of artificial intelligence (AI). By integrating existing technologies with AI, researchers can explore and develop new flavor substances in a digital environment, saving time and resources. More and more research will use AI and big data to enhance product flavor, improve product quality, meet consumer needs, and drive the industry toward a smarter and more sustainable future. In this review, we elaborate on the mechanisms of flavor recognition and their potential impact on nutritional regulation. With the increase of data accumulation and the development of internet information technology, food flavor databases and food ingredient databases have made great progress. These databases provide detailed information on the nutritional content, flavor molecules, and chemical properties of various food compounds, providing valuable data support for the rapid evaluation of flavor components and the construction of screening technology. With the popularization of AI in various fields, the field of food flavor has also ushered in new development opportunities. This review explores the mechanisms of flavor recognition and the role of AI in enhancing food flavor analysis through high-throughput omics data and screening technologies. AI algorithms offer a pathway to scientifically improve product formulations, thereby enhancing flavor and customized meals. Furthermore, it discusses the safety challenges of integrating AI into the food flavor industry.
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Affiliation(s)
- Zhiyong Cui
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Chengliang Qi
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Tianxing Zhou
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China
- Department of Bioinformatics, Faculty of Science, The University of Melbourne, Melbourne, Victoria, Australia
| | - Yanyang Yu
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Yueming Wang
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Zhiwei Zhang
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Yin Zhang
- Key Laboratory of Meat Processing of Sichuan, Chengdu University, Chengdu, China
| | - Wenli Wang
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Yuan Liu
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China
- School of Food Science and Engineering, Ningxia University, Yinchuan, China
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Tan S, Xin X, Wu D. ChatGPT in medicine: prospects and challenges: a review article. Int J Surg 2024; 110:3701-3706. [PMID: 38502861 PMCID: PMC11175750 DOI: 10.1097/js9.0000000000001312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 02/26/2024] [Indexed: 03/21/2024]
Abstract
It has been a year since the launch of Chat Generator Pre-Trained Transformer (ChatGPT), a generative artificial intelligence (AI) program. The introduction of this cross-generational product initially brought a huge shock to people with its incredible potential and then aroused increasing concerns among people. In the field of medicine, researchers have extensively explored the possible applications of ChatGPT and achieved numerous satisfactory results. However, opportunities and issues always come together. Problems have also been exposed during the applications of ChatGPT, requiring cautious handling, thorough consideration, and further guidelines for safe use. Here, the authors summarized the potential applications of ChatGPT in the medical field, including revolutionizing healthcare consultation, assisting patient management and treatment, transforming medical education, and facilitating clinical research. Meanwhile, the authors also enumerated researchers' concerns arising along with its broad and satisfactory applications. As it is irreversible that AI will gradually permeate every aspect of modern life, the authors hope that this review can not only promote people's understanding of the potential applications of ChatGPT in the future but also remind them to be more cautious about this "Pandora's Box" in the medical field. It is necessary to establish normative guidelines for its safe use in the medical field as soon as possible.
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Affiliation(s)
| | | | - Di Wu
- Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shijingshan, Beijing, China
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Kaba E, Solak M, Çeliker FB. The Role of Prompt Engineering in Radiology Applications of Generative AI. Acad Radiol 2024; 31:2641. [PMID: 38523008 DOI: 10.1016/j.acra.2024.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 03/03/2024] [Accepted: 03/03/2024] [Indexed: 03/26/2024]
Affiliation(s)
- Esat Kaba
- Department of Radiology, Recep Tayyip Erdogan University, Rize 53100, Turkey.
| | - Merve Solak
- Department of Radiology, Recep Tayyip Erdogan University, Rize 53100, Turkey
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