{"title":"人工智能增强的基于分子的精准肿瘤临床决策。","authors":"Jia Zeng, Md Abu Shufean","doi":"10.1042/ETLS20210220","DOIUrl":null,"url":null,"abstract":"<p><p>The rapid growth and decreasing cost of Next-generation sequencing (NGS) technologies have made it possible to conduct routine large panel genomic sequencing in many disease settings, especially in the oncology domain. Furthermore, it is now known that optimal disease management of patients depends on individualized cancer treatment guided by comprehensive molecular testing. However, translating results from molecular sequencing reports into actionable clinical insights remains a challenge to most clinicians. In this review, we discuss about some representative systems that leverage artificial intelligence (AI) to facilitate some processes of clinicians' decision making based upon molecular data, focusing on their application in precision oncology. Some limitations and pitfalls of the current application of AI in clinical decision making are also discussed.</p>","PeriodicalId":46394,"journal":{"name":"Emerging Topics in Life Sciences","volume":"5 6","pages":"757-764"},"PeriodicalIF":3.4000,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/f4/ad/ETLS-5-757.PMC8786281.pdf","citationCount":"4","resultStr":"{\"title\":\"Molecular-based precision oncology clinical decision making augmented by artificial intelligence.\",\"authors\":\"Jia Zeng, Md Abu Shufean\",\"doi\":\"10.1042/ETLS20210220\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The rapid growth and decreasing cost of Next-generation sequencing (NGS) technologies have made it possible to conduct routine large panel genomic sequencing in many disease settings, especially in the oncology domain. Furthermore, it is now known that optimal disease management of patients depends on individualized cancer treatment guided by comprehensive molecular testing. However, translating results from molecular sequencing reports into actionable clinical insights remains a challenge to most clinicians. In this review, we discuss about some representative systems that leverage artificial intelligence (AI) to facilitate some processes of clinicians' decision making based upon molecular data, focusing on their application in precision oncology. Some limitations and pitfalls of the current application of AI in clinical decision making are also discussed.</p>\",\"PeriodicalId\":46394,\"journal\":{\"name\":\"Emerging Topics in Life Sciences\",\"volume\":\"5 6\",\"pages\":\"757-764\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2021-12-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/f4/ad/ETLS-5-757.PMC8786281.pdf\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Emerging Topics in Life Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1042/ETLS20210220\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Emerging Topics in Life Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1042/ETLS20210220","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
Molecular-based precision oncology clinical decision making augmented by artificial intelligence.
The rapid growth and decreasing cost of Next-generation sequencing (NGS) technologies have made it possible to conduct routine large panel genomic sequencing in many disease settings, especially in the oncology domain. Furthermore, it is now known that optimal disease management of patients depends on individualized cancer treatment guided by comprehensive molecular testing. However, translating results from molecular sequencing reports into actionable clinical insights remains a challenge to most clinicians. In this review, we discuss about some representative systems that leverage artificial intelligence (AI) to facilitate some processes of clinicians' decision making based upon molecular data, focusing on their application in precision oncology. Some limitations and pitfalls of the current application of AI in clinical decision making are also discussed.