在传统东亚医学研究中应用人工智能的实用指南

IF 2.8 4区医学 Q2 INTEGRATIVE & COMPLEMENTARY MEDICINE

Integrative Medicine Research Pub Date : 2024-07-07 DOI:10.1016/j.imr.2024.101067

Hyojin Bae , Sa-Yoon Park , Chang-Eop Kim

{"title":"在传统东亚医学研究中应用人工智能的实用指南","authors":"Hyojin Bae , Sa-Yoon Park , Chang-Eop Kim","doi":"10.1016/j.imr.2024.101067","DOIUrl":null,"url":null,"abstract":"<div><p>In this paper, we present a comprehensive guide for implementing artificial intelligence (AI) techniques in traditional East Asian medicine (TEAM) research. We cover essential aspects of the AI model development pipeline, including research objective establishment, data collection and preprocessing, model selection, evaluation, and interpretation. The unique considerations in applying AI to TEAM datasets, such as data scarcity, imbalance, and model interpretability, are discussed. We provide practical tips and recommendations based on best practices and our own experience. The potential of large language models in TEAM research is also highlighted. Finally, we discuss the challenges and future directions of AI application in TEAM, emphasizing the need for standardized data collection and sharing platforms.</p></div>","PeriodicalId":13644,"journal":{"name":"Integrative Medicine Research","volume":"13 3","pages":"Article 101067"},"PeriodicalIF":2.8000,"publicationDate":"2024-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213422024000477/pdfft?md5=9c38cb96d3ef0d68d7cfe03c59026a48&pid=1-s2.0-S2213422024000477-main.pdf","citationCount":"0","resultStr":"{\"title\":\"A practical guide to implementing artificial intelligence in traditional East Asian medicine research\",\"authors\":\"Hyojin Bae , Sa-Yoon Park , Chang-Eop Kim\",\"doi\":\"10.1016/j.imr.2024.101067\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In this paper, we present a comprehensive guide for implementing artificial intelligence (AI) techniques in traditional East Asian medicine (TEAM) research. We cover essential aspects of the AI model development pipeline, including research objective establishment, data collection and preprocessing, model selection, evaluation, and interpretation. The unique considerations in applying AI to TEAM datasets, such as data scarcity, imbalance, and model interpretability, are discussed. We provide practical tips and recommendations based on best practices and our own experience. The potential of large language models in TEAM research is also highlighted. Finally, we discuss the challenges and future directions of AI application in TEAM, emphasizing the need for standardized data collection and sharing platforms.</p></div>\",\"PeriodicalId\":13644,\"journal\":{\"name\":\"Integrative Medicine Research\",\"volume\":\"13 3\",\"pages\":\"Article 101067\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-07-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2213422024000477/pdfft?md5=9c38cb96d3ef0d68d7cfe03c59026a48&pid=1-s2.0-S2213422024000477-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Integrative Medicine Research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2213422024000477\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"INTEGRATIVE & COMPLEMENTARY MEDICINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Integrative Medicine Research","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2213422024000477","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INTEGRATIVE & COMPLEMENTARY MEDICINE","Score":null,"Total":0}

引用次数: 0

摘要

本文介绍了在传统东亚医学（TEAM）研究中实施人工智能（AI）技术的综合指南。我们介绍了人工智能模型开发流程的基本方面，包括研究目标的确立、数据收集和预处理、模型选择、评估和解释。讨论了将人工智能应用于 TEAM 数据集的独特考虑因素，如数据稀缺性、不平衡性和模型可解释性。我们将根据最佳实践和自身经验提供实用的提示和建议。我们还强调了大型语言模型在 TEAM 研究中的潜力。最后，我们讨论了在 TEAM 中应用人工智能的挑战和未来方向，强调了标准化数据收集和共享平台的必要性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

A practical guide to implementing artificial intelligence in traditional East Asian medicine research

In this paper, we present a comprehensive guide for implementing artificial intelligence (AI) techniques in traditional East Asian medicine (TEAM) research. We cover essential aspects of the AI model development pipeline, including research objective establishment, data collection and preprocessing, model selection, evaluation, and interpretation. The unique considerations in applying AI to TEAM datasets, such as data scarcity, imbalance, and model interpretability, are discussed. We provide practical tips and recommendations based on best practices and our own experience. The potential of large language models in TEAM research is also highlighted. Finally, we discuss the challenges and future directions of AI application in TEAM, emphasizing the need for standardized data collection and sharing platforms.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Integrative Medicine Research Medicine-Complementary and Alternative Medicine

CiteScore

6.50

自引率

2.90%

发文量

审稿时长

12 weeks

期刊介绍： Integrative Medicine Research (IMR) is a quarterly, peer-reviewed journal focused on scientific research for integrative medicine including traditional medicine (emphasis on acupuncture and herbal medicine), complementary and alternative medicine, and systems medicine. The journal includes papers on basic research, clinical research, methodology, theory, computational analysis and modelling, topical reviews, medical history, education and policy based on physiology, pathology, diagnosis and the systems approach in the field of integrative medicine.