Ai schaumburg transcripts12/4/2023 Instead, data is extracted from personal electronic health records (EHRs) Footnote 2, that is, digitalised health data that is generated in the process of the medical treatment of a human being. In most cases, the data that are used to train medical AI are not generated for the purpose of medical AI development. Multiple other public and private databases alike contain unique compositions of data. This database contains over 110,000 chest X-ray images and corresponding diagnostic records from 30,805 individuals. CheXNet, for example, trained its diagnostic abilities with the help of ChestX-Ray14 Footnote 1, a publicly available database. data sets that are used by machine learning algorithms to optimise their modelling abilities. To accomplish their tasks, medical AIs need to access and process different types of training data i.a. The various application possibilities underline the algorithms potential benefits. Medical AI can also be used to optimise medical research, patient management, health care systems, and drug development. It can be used to predict a healthy person’s risk of falling ill or a sick person’s risk of dying. Medical AI is used to improve diagnostics, therapies, nursing practices, preventive care, and emergency medicine. CheXNet is one of many examples of artificial intelligence (AI) in medicine. The algorithm used the labelled data to extract new recognition patterns for X-ray diagnostics. How did CheXNet learn to do this? In a supervised training process, the algorithm accessed a database containing chest X-ray images, with each image linked to diagnostic metadata. In 2017, Stanford University AI CheXNet diagnosed pneumonia more accurately in a test run than three out of four competing human radiologists. Given this result, the article proposes an alternative civic responsibility approach that can attribute different responsibilities to different social groups and individuals and that can contextualise those responsibilities for the purpose of medical AI development. This article critiques all three arguments because they either derive a civic duty from premises that do not apply to the medical AI context, or they rely on inappropriate analogies, or they ignore significant risks entailed by the EHR sharing process and the use of medical AI. There are three main arguments in favour of a civic duty to support certain developments in medical AI by sharing EHRs: the ‘rule to rescue argument’, the ‘low risks, high benefits argument’, and the ‘property rights argument’. This article sheds light on the correlation between two normative discourses concerning informed consent for secondary health record use and the development and use of medical AI. In the medical AI context, the common arguments for such a duty have not been subjected to a comprehensive challenge. For this reason, a number of scholars advocate a moral civic duty to share electronic health records (EHRs) that overrides IC requirements in certain contexts. This regulation, however, is believed to slow down or even prevent vital medical research, including AI development. Usually, health data can only be put to a secondary use if data subjects provide their informed consent (IC). To develop innovative medical AI, it is necessary to repurpose data that are primarily generated in and for the health care context. Medical artificial intelligence (AI) is considered to be one of the most important assets for the future of innovative individual and public health care.
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