{"title":"人工智能与医学的交叉:技术时代的侵权责任","authors":"Kyle T. Jorstad","doi":"10.21037/jmai-20-57","DOIUrl":null,"url":null,"abstract":": This note presents an analysis of the medico-legal and bioethical risks posed by the incorporation of artificial intelligence (AI) and machine learning into clinical radiology practice, with specific focus on the field of mammography. The analysis presents an overview of the current medical malpractice framework relative to mammography; examines the fitness of current legal frameworks for apportioning liability in cases of injury resulting from errors by machine learning tools; evaluates various options for addressing the malpractice model’s gaps as AI is incorporated into clinical patient care; and provides means by which the healthcare industry may both minimize short-term liability for machine learning error, while ensuring that neither the public nor the regulatory framework are unnecessarily biased against the use of AI in medicine.","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Intersection of artificial intelligence and medicine: tort liability in the technological age\",\"authors\":\"Kyle T. Jorstad\",\"doi\":\"10.21037/jmai-20-57\",\"DOIUrl\":null,\"url\":null,\"abstract\":\": This note presents an analysis of the medico-legal and bioethical risks posed by the incorporation of artificial intelligence (AI) and machine learning into clinical radiology practice, with specific focus on the field of mammography. The analysis presents an overview of the current medical malpractice framework relative to mammography; examines the fitness of current legal frameworks for apportioning liability in cases of injury resulting from errors by machine learning tools; evaluates various options for addressing the malpractice model’s gaps as AI is incorporated into clinical patient care; and provides means by which the healthcare industry may both minimize short-term liability for machine learning error, while ensuring that neither the public nor the regulatory framework are unnecessarily biased against the use of AI in medicine.\",\"PeriodicalId\":73815,\"journal\":{\"name\":\"Journal of medical artificial intelligence\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of medical artificial intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21037/jmai-20-57\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of medical artificial intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21037/jmai-20-57","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Intersection of artificial intelligence and medicine: tort liability in the technological age
: This note presents an analysis of the medico-legal and bioethical risks posed by the incorporation of artificial intelligence (AI) and machine learning into clinical radiology practice, with specific focus on the field of mammography. The analysis presents an overview of the current medical malpractice framework relative to mammography; examines the fitness of current legal frameworks for apportioning liability in cases of injury resulting from errors by machine learning tools; evaluates various options for addressing the malpractice model’s gaps as AI is incorporated into clinical patient care; and provides means by which the healthcare industry may both minimize short-term liability for machine learning error, while ensuring that neither the public nor the regulatory framework are unnecessarily biased against the use of AI in medicine.