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The three ghosts of medical AI: Can the black-box present deliver?
Quinn, Thomas P; Jacobs, Stephan; Senadeera, Manisha; Le, Vuong; Coghlan, Simon.
Afiliación
  • Quinn TP; Applied Artificial Intelligence Institute, Deakin University, Geelong, Australia. Electronic address: contacttomquinn@gmail.com.
  • Jacobs S; Applied Artificial Intelligence Institute, Deakin University, Geelong, Australia.
  • Senadeera M; Applied Artificial Intelligence Institute, Deakin University, Geelong, Australia.
  • Le V; Applied Artificial Intelligence Institute, Deakin University, Geelong, Australia.
  • Coghlan S; Centre for AI and Digital Ethics, School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia.
Artif Intell Med ; 124: 102158, 2022 02.
Article en En | MEDLINE | ID: mdl-34511267
ABSTRACT
Our title alludes to the three Christmas ghosts encountered by Ebenezer Scrooge in A Christmas Carol, who guide Ebenezer through the past, present, and future of Christmas holiday events. Similarly, our article takes readers through a journey of the past, present, and future of medical AI. In doing so, we focus on the crux of modern machine learning the reliance on powerful but intrinsically opaque models. When applied to the healthcare domain, these models fail to meet the needs for transparency that their clinician and patient end-users require. We review the implications of this failure, and argue that opaque models (1) lack quality assurance, (2) fail to elicit trust, and (3) restrict physician-patient dialogue. We then discuss how upholding transparency in all aspects of model design and model validation can help ensure the reliability and success of medical AI.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Aprendizaje Automático Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Artif Intell Med Asunto de la revista: INFORMATICA MEDICA Año: 2022 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Aprendizaje Automático Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Artif Intell Med Asunto de la revista: INFORMATICA MEDICA Año: 2022 Tipo del documento: Article
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