Zhen Ling Teo, Ann Kwee, John Cw Lim, Carolyn Sp Lam, Dean Ho, Sebastian Maurer-Stroh, Yi Su, Simon Chesterman, Tsuhan Chen, Chorh Chuan Tan, Tien Yin Wong, Kee Yuan Ngiam, Cher Heng Tan, Danny Soon, May Ling Choong, Raymond Chua, Sutowo Wong, Colin Lim, Wei Yang Cheong, Daniel Sw Ting
{"title":"医疗保健中的人工智能创新:从实验室到床边的临床转化报告指南的相关性","authors":"Zhen Ling Teo, Ann Kwee, John Cw Lim, Carolyn Sp Lam, Dean Ho, Sebastian Maurer-Stroh, Yi Su, Simon Chesterman, Tsuhan Chen, Chorh Chuan Tan, Tien Yin Wong, Kee Yuan Ngiam, Cher Heng Tan, Danny Soon, May Ling Choong, Raymond Chua, Sutowo Wong, Colin Lim, Wei Yang Cheong, Daniel Sw Ting","doi":"10.47102/annals-acadmedsg.2022452","DOIUrl":null,"url":null,"abstract":"<p><p>Artificial intelligence (AI) and digital innovation are transforming healthcare. Technologies such as machine learning in image analysis, natural language processing in medical chatbots and electronic medical record extraction have the potential to improve screening, diagnostics and prognostication, leading to precision medicine and preventive health. However, it is crucial to ensure that AI research is conducted with scientific rigour to facilitate clinical implementation. Therefore, reporting guidelines have been developed to standardise and streamline the development and validation of AI technologies in health. This commentary proposes a structured approach to utilise these reporting guidelines for the translation of promising AI techniques from research and development into clinical translation, and eventual widespread implementation from bench to bedside.</p>","PeriodicalId":50774,"journal":{"name":"Annals Academy of Medicine Singapore","volume":null,"pages":null},"PeriodicalIF":5.2000,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence innovation in healthcare: Relevance of reporting guidelines for clinical translation from bench to bedside.\",\"authors\":\"Zhen Ling Teo, Ann Kwee, John Cw Lim, Carolyn Sp Lam, Dean Ho, Sebastian Maurer-Stroh, Yi Su, Simon Chesterman, Tsuhan Chen, Chorh Chuan Tan, Tien Yin Wong, Kee Yuan Ngiam, Cher Heng Tan, Danny Soon, May Ling Choong, Raymond Chua, Sutowo Wong, Colin Lim, Wei Yang Cheong, Daniel Sw Ting\",\"doi\":\"10.47102/annals-acadmedsg.2022452\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Artificial intelligence (AI) and digital innovation are transforming healthcare. Technologies such as machine learning in image analysis, natural language processing in medical chatbots and electronic medical record extraction have the potential to improve screening, diagnostics and prognostication, leading to precision medicine and preventive health. However, it is crucial to ensure that AI research is conducted with scientific rigour to facilitate clinical implementation. Therefore, reporting guidelines have been developed to standardise and streamline the development and validation of AI technologies in health. This commentary proposes a structured approach to utilise these reporting guidelines for the translation of promising AI techniques from research and development into clinical translation, and eventual widespread implementation from bench to bedside.</p>\",\"PeriodicalId\":50774,\"journal\":{\"name\":\"Annals Academy of Medicine Singapore\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.2000,\"publicationDate\":\"2023-04-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals Academy of Medicine Singapore\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.47102/annals-acadmedsg.2022452\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals Academy of Medicine Singapore","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.47102/annals-acadmedsg.2022452","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Medicine","Score":null,"Total":0}
Artificial intelligence innovation in healthcare: Relevance of reporting guidelines for clinical translation from bench to bedside.
Artificial intelligence (AI) and digital innovation are transforming healthcare. Technologies such as machine learning in image analysis, natural language processing in medical chatbots and electronic medical record extraction have the potential to improve screening, diagnostics and prognostication, leading to precision medicine and preventive health. However, it is crucial to ensure that AI research is conducted with scientific rigour to facilitate clinical implementation. Therefore, reporting guidelines have been developed to standardise and streamline the development and validation of AI technologies in health. This commentary proposes a structured approach to utilise these reporting guidelines for the translation of promising AI techniques from research and development into clinical translation, and eventual widespread implementation from bench to bedside.
期刊介绍:
The Annals is the official journal of the Academy of Medicine, Singapore. Established in 1972, Annals is the leading medical journal in Singapore which aims to publish novel findings from clinical research as well as medical practices that can benefit the medical community.