Artificial Intelligence in Health Care

Abstract

The promise of artificial intelligence (AI) in health care offers substantial opportunities to improve patient and clinical team outcomes, reduce costs, and influence population health. Current data generation greatly exceeds human cognitive capacity to effectively manage information, and AI is likely to have an important and complementary role to human cognition to support delivery of personalized health care. 1 For example, recent innovations in AI have shown high levels of accuracy in imaging and signal detection tasks and are considered among the most mature tools in this domain. 2 However, there are challenges in realizing the potential for AI in health care. Disconnects between reality and expectations have led to prior precipitous declines in use of the technology, termed AI winters, and another such event is possible, especially in health care. 3 Today, AI has outsized market expectations and technology sector investments. Current challenges include using biased data for AI model development, applying AI outside of populations represented in the training and validation data sets, disregarding the effects of possible unintended consequences on care or the patientclinician relationship, and limited data about actual effects on patient outcomes and cost of care.AI in Healthcare: The Hope, The Hype, The Promise, The Peril, a publication by the National Academy of Medicine (NAM), synthesizes current knowledge and offers a reference document for the responsible development, implementation, and maintenance of AI in the clinical enterprise. 4 The publication outlines current and near-term AI solutions; highlights the challenges, limi-Health care is at a critical juncture for the safe and effective use of AI algorithms and tools in supporting the health of patients.

Review of Clinical Research Informatics

“…The Journal of the American Medical Association has paid close attention to such questions. We note in particular a guide to reading the literature 10 , an accompanying editorial 11 , and a viewpoint review 12 of the National Academy of Medicine’s comprehensive exploration of AI in healthcare 13 . Possible biases in the design and development of AI systems in conjunction with EHRs have also been explored 14 , as has their remediation 15 and the potential legal liability risk for a provider using AI 16 .…”

Predicting Old-age Mortality Using Principal Component Analysis: Results from a National Panel Survey in Korea

“…In addition, we suggest that studies revealing multiple grouped conditions associated with mortality may improve a value-based healthcare strategy, along with the advanced estimation model using AI. Since 2019, the Centers for Medicare and Medicaid Services (CMS) has launched the projects for AI Health Outcomes Challenge and offered federal grants and contracts to innovators to demonstrate how AI tools—such as deep learning and neural networks—can be used to predict unplanned hospital and skilled nursing facility admissions as well as adverse events [ 34 , 35 ]. A variety of lifestyle and health data enable us to analyze negative health outcomes at once and prevent such harmful triggers as soon as possible.…”

Identifying Ethical Considerations for Machine Learning Healthcare Applications

“…ML-HCAs, like all new technologies, present uncertainty regarding their future impact. Ethical frameworks that focus on articulating guiding principles without first systematically identifying potential problems (Challen et al 2019;Matheny et al 2019Matheny et al , 2020 do not specifically address this uncertainty. While various conceptual frameworks have been proposed to guide anticipatory ethical analyses of emerging technologies (Brey 2012) or to ascertain the values inherent in design approaches (Shilton 2018), a common general feature of these methods is the importance of having a systematic approach guided by an underlying evaluative framework to identify key considerations across as full a range as is possible of potential impacts.…”

“…With the FDA authorization of an autonomous artificial intelligence diagnostic system based on machine learning (ML), which employs algorithms that can learn from large data sets and make predictions without being explicitly programmed, ML healthcare applications (ML-HCAs) have transitioned from being an enticing future possibility to a present clinical reality (Abr amoff et al 2018; Commissioner Office of the FDA 2020). Almost certainly, ML-HCAs will have a substantial impact on healthcare processes, quality, cost, and access, and in so doing will raise specific and perhaps unique ethical considerations and concerns in the healthcare context (Obermeyer and Emanuel 2016;(Rajkomar et al 2019;Maddox et al 2019;Matheny et al 2019Matheny et al , 2020. This has been the case in non-healthcare contexts (Char et al 2018;Bostrom and Yudkowski 2011), where ML implementation has generated toughening scrutiny due to scandals regarding how large repositories of private data have been sold and used (Rosenberg and Frenkel 2018), how the ML design of algorithmic flight controls resulted in accidents (Nicas et al 2019), and how computer-assisted prison sentencing guidelines perpetuate racial bias (Angwin et al 2016), to name but a few of the growing number of examples.…”