Uncertainty, Evidence, and the Integration of Machine Learning into Medical Practice.
In light of recent advances in machine learning for medical applications, the automation of medical diagnostics is imminent. That said, before machine learning algorithms find their way into clinical practice, various problems at the epistemic level need to be overcome. In this paper, we discuss dif...
| Publicado en: | Journal of Medicine & Philosophy Vol. 48; no. 1; pp. 84 - 98 |
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| Autores principales: | , |
| Formato: | Artículo |
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Oxford University Press / USA
Feb2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=161964068&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 161964068 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 03605310 MPS jtl: Journal of Medicine & Philosophy issn: 03605310 maglogo: N pubinfo: dt: Feb2023 vid: 48 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 161964068 10.1093/jmp/jhac034 ppf: 84 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P size: 256KB tig: atl: Uncertainty, Evidence, and the Integration of Machine Learning into Medical Practice. aug: au: Grote, Thomas Berens, Philipp affil: University of Tübingen , Tübingen , Germany su: Deep learning Machine learning Medical practice Bored piles Trust sug: subj: Deep learning Machine learning Medical practice Bored piles Trust keyword: black box problem evidence machine learning medical diagnosis uncertainty ab: In light of recent advances in machine learning for medical applications, the automation of medical diagnostics is imminent. That said, before machine learning algorithms find their way into clinical practice, various problems at the epistemic level need to be overcome. In this paper, we discuss different sources of uncertainty arising for clinicians trying to evaluate the trustworthiness of algorithmic evidence when making diagnostic judgments. Thereby, we examine many of the limitations of current machine learning algorithms (with deep learning in particular) and highlight their relevance for medical diagnostics. Among the problems we inspect are the theoretical foundations of deep learning (which are not yet adequately understood), the opacity of algorithmic decisions, and the vulnerabilities of machine learning models, as well as concerns regarding the quality of medical data used to train the models. Building on this, we discuss different desiderata for an uncertainty amelioration strategy that ensures that the integration of machine learning into clinical settings proves to be medically beneficial in a meaningful way. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 Journal of Medicine and Philosophy Inc.. item: Journal of Medicine & Philosophy holder: Oxford University Press / USA dt: @attributes: year: 2023 holdings: @attributes: islocal: N |
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