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The automation of doctors and machines: A classification for AI in medicine (ADAM framework)

Kazzazi, Fawz; (2021) The automation of doctors and machines: A classification for AI in medicine (ADAM framework). Future Healthcare Journal , 8 (2) e257-e262. 10.7861/fhj.2020-0189. Green open access

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Abstract

The advances in artificial intelligence (AI) provide an opportunity to expand the frontier of medicine to improve diagnosis, efficiency and management. By extension of being able to perform any task that a human could, a machine that meets the requirements of artificial general intelligence (‘strong’ AI; AGI) possesses the basic necessities to perform as, or at least qualify to become, a doctor. In this emerging field, this article explores the distinctions between doctors and AGI, and the prerequisites for AGI performing as clinicians. In doing so, it necessitates the requirement for a classification of medical AI and prepares for the development of AGI. With its imminent arrival, it is beneficial to create a framework from which leading institutions can define specific criteria for AGI.

Type: Article
Title: The automation of doctors and machines: A classification for AI in medicine (ADAM framework)
Open access status: An open access version is available from UCL Discovery
DOI: 10.7861/fhj.2020-0189
Publisher version: http://dx.doi.org/10.7861/fhj.2020-0189
Language: English
Additional information: © Royal College of Physicians 2021 under a Creative Commons license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: medical technology, artificial intelligence, AI, clinical governance
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > UCL Medical School
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > UCL Medical School > Clinical and Professional Practice Unit
URI: https://discovery-pp.ucl.ac.uk/id/eprint/10194794
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