Conceptualising Artificial Intelligence as a Digital Healthcare Innovation: An Introductory Review

Anmol Arora School of Clinical Medicine, University of Cambridge, Cambridge, UKCorrespondence: Anmol AroraSchool of Clinical Medicine, University of Cambridge, Trinity Hall, Trinity Lane, Cambridge, CB2 1TJ, UKTel +441223 332500Email aa957@cam.ac.ukAbstract: Artificial intelligence (AI) is widely re...

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Autor principal:	Arora A
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2020
Materias:	machine learning data diagnostic algorithms artificial intelligence innovation Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/09774033b1f949afa29b5670a5d61ba5
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spelling	oai:doaj.org-article:09774033b1f949afa29b5670a5d61ba52021-12-02T10:06:59ZConceptualising Artificial Intelligence as a Digital Healthcare Innovation: An Introductory Review1179-1470https://doaj.org/article/09774033b1f949afa29b5670a5d61ba52020-08-01T00:00:00Zhttps://www.dovepress.com/conceptualising-artificial-intelligence-as-a-digital-healthcare-innova-peer-reviewed-article-MDERhttps://doaj.org/toc/1179-1470Anmol Arora School of Clinical Medicine, University of Cambridge, Cambridge, UKCorrespondence: Anmol AroraSchool of Clinical Medicine, University of Cambridge, Trinity Hall, Trinity Lane, Cambridge, CB2 1TJ, UKTel +441223 332500Email aa957@cam.ac.ukAbstract: Artificial intelligence (AI) is widely recognised as a transformative innovation and is already proving capable of outperforming human clinicians in the diagnosis of specific medical conditions, especially in image analysis within dermatology and radiology. These abilities are enhanced by the capacity of AI systems to learn from patient records, genomic information and real-time patient data. Uses of AI range from integrating with robotics to creating training material for clinicians. Whilst AI research is mounting, less attention has been paid to the practical implications on healthcare services and potential barriers to implementation. AI is recognised as a “Software as a Medical Device (SaMD)” and is increasingly becoming a topic of interest for regulators. Unless the introduction of AI is carefully considered and gradual, there are risks of automation bias, overdependence and long-term staffing problems. This is in addition to already well-documented generic risks associated with AI, such as data privacy, algorithmic biases and corrigibility. AI is able to potentiate innovations which preceded it, using Internet of Things, digitisation of patient records and genetic data as data sources. These synergies are important in both realising the potential of AI and utilising the potential of the data. As machine learning systems begin to cross-examine an array of databases, we must ensure that clinicians retain autonomy over the diagnostic process and understand the algorithmic processes generating diagnoses. This review uses established management literature to explore artificial intelligence as a digital healthcare innovation and highlight potential risks and opportunities.Keywords: machine learning, data, diagnostic algorithms, artificial intelligence, innovationArora ADove Medical Pressarticlemachine learningdatadiagnostic algorithmsartificial intelligenceinnovationMedical technologyR855-855.5ENMedical Devices: Evidence and Research, Vol Volume 13, Pp 223-230 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning data diagnostic algorithms artificial intelligence innovation Medical technology R855-855.5
spellingShingle	machine learning data diagnostic algorithms artificial intelligence innovation Medical technology R855-855.5 Arora A Conceptualising Artificial Intelligence as a Digital Healthcare Innovation: An Introductory Review
description	Anmol Arora School of Clinical Medicine, University of Cambridge, Cambridge, UKCorrespondence: Anmol AroraSchool of Clinical Medicine, University of Cambridge, Trinity Hall, Trinity Lane, Cambridge, CB2 1TJ, UKTel +441223 332500Email aa957@cam.ac.ukAbstract: Artificial intelligence (AI) is widely recognised as a transformative innovation and is already proving capable of outperforming human clinicians in the diagnosis of specific medical conditions, especially in image analysis within dermatology and radiology. These abilities are enhanced by the capacity of AI systems to learn from patient records, genomic information and real-time patient data. Uses of AI range from integrating with robotics to creating training material for clinicians. Whilst AI research is mounting, less attention has been paid to the practical implications on healthcare services and potential barriers to implementation. AI is recognised as a “Software as a Medical Device (SaMD)” and is increasingly becoming a topic of interest for regulators. Unless the introduction of AI is carefully considered and gradual, there are risks of automation bias, overdependence and long-term staffing problems. This is in addition to already well-documented generic risks associated with AI, such as data privacy, algorithmic biases and corrigibility. AI is able to potentiate innovations which preceded it, using Internet of Things, digitisation of patient records and genetic data as data sources. These synergies are important in both realising the potential of AI and utilising the potential of the data. As machine learning systems begin to cross-examine an array of databases, we must ensure that clinicians retain autonomy over the diagnostic process and understand the algorithmic processes generating diagnoses. This review uses established management literature to explore artificial intelligence as a digital healthcare innovation and highlight potential risks and opportunities.Keywords: machine learning, data, diagnostic algorithms, artificial intelligence, innovation
format	article
author	Arora A
author_facet	Arora A
author_sort	Arora A
title	Conceptualising Artificial Intelligence as a Digital Healthcare Innovation: An Introductory Review
title_short	Conceptualising Artificial Intelligence as a Digital Healthcare Innovation: An Introductory Review
title_full	Conceptualising Artificial Intelligence as a Digital Healthcare Innovation: An Introductory Review
title_fullStr	Conceptualising Artificial Intelligence as a Digital Healthcare Innovation: An Introductory Review
title_full_unstemmed	Conceptualising Artificial Intelligence as a Digital Healthcare Innovation: An Introductory Review
title_sort	conceptualising artificial intelligence as a digital healthcare innovation: an introductory review
publisher	Dove Medical Press
publishDate	2020
url	https://doaj.org/article/09774033b1f949afa29b5670a5d61ba5
work_keys_str_mv	AT aroraa conceptualisingartificialintelligenceasadigitalhealthcareinnovationanintroductoryreview
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Conceptualising Artificial Intelligence as a Digital Healthcare Innovation: An Introductory Review

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