Artificial Intelligence, Heuristic Biases, and the Optimization of Health Outcomes: Cautionary Optimism

The use of artificial intelligence (AI) and machine learning (ML) in clinical care offers great promise to improve patient health outcomes and reduce health inequity across patient populations. However, inherent biases in these applications, and the subsequent potential risk of harm can limit curren...

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Autores principales:	Michael Feehan, Leah A. Owen, Ian M. McKinnon, Margaret M. DeAngelis
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	heuristics bias artificial intelligence machine learning health outcomes population health Medicine R
Acceso en línea:	https://doaj.org/article/6d2110e77ac846a6bb219b2d18b2c7cb
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spelling	oai:doaj.org-article:6d2110e77ac846a6bb219b2d18b2c7cb2021-11-25T18:01:21ZArtificial Intelligence, Heuristic Biases, and the Optimization of Health Outcomes: Cautionary Optimism10.3390/jcm102252842077-0383https://doaj.org/article/6d2110e77ac846a6bb219b2d18b2c7cb2021-11-01T00:00:00Zhttps://www.mdpi.com/2077-0383/10/22/5284https://doaj.org/toc/2077-0383The use of artificial intelligence (AI) and machine learning (ML) in clinical care offers great promise to improve patient health outcomes and reduce health inequity across patient populations. However, inherent biases in these applications, and the subsequent potential risk of harm can limit current use. Multi-modal workflows designed to minimize these limitations in the development, implementation, and evaluation of ML systems in real-world settings are needed to improve efficacy while reducing bias and the risk of potential harms. Comprehensive consideration of rapidly evolving AI technologies and the inherent risks of bias, the expanding volume and nature of data sources, and the evolving regulatory landscapes, can contribute meaningfully to the development of AI-enhanced clinical decision making and the reduction in health inequity.Michael FeehanLeah A. OwenIan M. McKinnonMargaret M. DeAngelisMDPI AGarticleheuristicsbiasartificial intelligencemachine learninghealth outcomespopulation healthMedicineRENJournal of Clinical Medicine, Vol 10, Iss 5284, p 5284 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	heuristics bias artificial intelligence machine learning health outcomes population health Medicine R
spellingShingle	heuristics bias artificial intelligence machine learning health outcomes population health Medicine R Michael Feehan Leah A. Owen Ian M. McKinnon Margaret M. DeAngelis Artificial Intelligence, Heuristic Biases, and the Optimization of Health Outcomes: Cautionary Optimism
description	The use of artificial intelligence (AI) and machine learning (ML) in clinical care offers great promise to improve patient health outcomes and reduce health inequity across patient populations. However, inherent biases in these applications, and the subsequent potential risk of harm can limit current use. Multi-modal workflows designed to minimize these limitations in the development, implementation, and evaluation of ML systems in real-world settings are needed to improve efficacy while reducing bias and the risk of potential harms. Comprehensive consideration of rapidly evolving AI technologies and the inherent risks of bias, the expanding volume and nature of data sources, and the evolving regulatory landscapes, can contribute meaningfully to the development of AI-enhanced clinical decision making and the reduction in health inequity.
format	article
author	Michael Feehan Leah A. Owen Ian M. McKinnon Margaret M. DeAngelis
author_facet	Michael Feehan Leah A. Owen Ian M. McKinnon Margaret M. DeAngelis
author_sort	Michael Feehan
title	Artificial Intelligence, Heuristic Biases, and the Optimization of Health Outcomes: Cautionary Optimism
title_short	Artificial Intelligence, Heuristic Biases, and the Optimization of Health Outcomes: Cautionary Optimism
title_full	Artificial Intelligence, Heuristic Biases, and the Optimization of Health Outcomes: Cautionary Optimism
title_fullStr	Artificial Intelligence, Heuristic Biases, and the Optimization of Health Outcomes: Cautionary Optimism
title_full_unstemmed	Artificial Intelligence, Heuristic Biases, and the Optimization of Health Outcomes: Cautionary Optimism
title_sort	artificial intelligence, heuristic biases, and the optimization of health outcomes: cautionary optimism
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/6d2110e77ac846a6bb219b2d18b2c7cb
work_keys_str_mv	AT michaelfeehan artificialintelligenceheuristicbiasesandtheoptimizationofhealthoutcomescautionaryoptimism AT leahaowen artificialintelligenceheuristicbiasesandtheoptimizationofhealthoutcomescautionaryoptimism AT ianmmckinnon artificialintelligenceheuristicbiasesandtheoptimizationofhealthoutcomescautionaryoptimism AT margaretmdeangelis artificialintelligenceheuristicbiasesandtheoptimizationofhealthoutcomescautionaryoptimism
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Artificial Intelligence, Heuristic Biases, and the Optimization of Health Outcomes: Cautionary Optimism

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