A governance model for the application of AI in health care

Reddy, Sandeep; Allan, Sonia; Coghlan, Simon; Cooper, Paul

doi:10.1093/jamia/ocz192

Cited by 412 publications

(348 citation statements)

References 22 publications

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“…An AI programme can provide a decision or prediction after learning patterns from data, but the interpretation and real-world implementation of AI models requires human expertise. Humans ultimately must decide how AI models should be integrated into clinical practice ( Bærøe et al , 2020 ; Reddy et al , 2020 ).…”

Section: Augmented Intelligence: the Interplay Between Human Expertismentioning

confidence: 99%

Artificial intelligence for clinical decision support in neurology

Pedersen

Verspoor

Jenkinson

et al. 2020

Brain Communications

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Abstract Artificial intelligence is one of the most exciting methodological shifts in our era. It holds the potential to transform healthcare as we know it, to a system where humans and machines work together to provide better treatment for our patients. It is now clear that cutting edge artificial intelligence models in conjunction with high-quality clinical data will lead to improved prognostic and diagnostic models in neurological disease, facilitating expert-level clinical decision tools across healthcare settings. Despite the clinical promise of artificial intelligence, machine and deep learning algorithms are not a one-size-fits-all solution for all types of clinical data and questions. In this paper, we provide an overview of the core concepts of artificial intelligence, particularly contemporary deep learning methods, to give clinician and neuroscience researchers an appreciation of how artificial intelligence can be harnessed to support clinical decisions. We clarify and emphasise the data quality and the human expertise needed to build robust clinical artificial intelligence models in neurology. Given that artificial intelligence is a rapidly evolving field, we take the opportunity to iterate important ethical principles to guide the field of medicine is it moves into an artificial intelligence enhanced future.

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Section: Augmented Intelligence: the Interplay Between Human Expertismentioning

confidence: 99%

Artificial intelligence for clinical decision support in neurology

Pedersen

Verspoor

Jenkinson

et al. 2020

Brain Communications

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“…Social justice Concerns about how these new technologies might affect the distribution of the benefits and burdens related to AI use in healthcare [3,9,16,17,19,[23][24][25][26][27][28][29].…”

Section: Parental Concerns Descriptionmentioning

confidence: 99%

Parental Attitudes toward Artificial Intelligence-Driven Precision Medicine Technologies in Pediatric Healthcare

et al. 2020

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Precision medicine relies upon artificial intelligence (AI)-driven technologies that raise ethical and practical concerns. In this study, we developed and validated a measure of parental openness and concerns with AI-driven technologies in their child’s healthcare. In this cross-sectional survey, we enrolled parents of children <18 years in 2 rounds for exploratory (n = 418) and confirmatory (n = 386) factor analysis. We developed a 12-item measure of parental openness to AI-driven technologies, and a 33-item measure identifying concerns that parents found important when considering these technologies. We also evaluated associations between openness and attitudes, beliefs, personality traits, and demographics. Parents (N = 804) reported mean openness to AI-driven technologies of M = 3.4/5, SD = 0.9. We identified seven concerns that parents considered important when evaluating these technologies: quality/accuracy, privacy, shared decision making, convenience, cost, human element of care, and social justice. In multivariable linear regression, parental openness was positively associated with quality (beta = 0.23), convenience (beta = 0.16), and cost (beta = 0.11), as well as faith in technology (beta = 0.23) and trust in health information systems (beta = 0.12). Parental openness was negatively associated with the perceived importance of shared decision making (beta = −0.16) and being female (beta = −0.12). Developers might support parental openness by addressing these concerns during the development and implementation of novel AI-driven technologies.

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“…AI offers a powerful toolkit for the rapid and safe automation of tedious or repetitive tasks and deep analysis beyond the capability of the human mind and, in doing so, provides time and energy for the physicians to more expertly review patient data and to communicate with the patient (5). Integrating AI solutions into the patient care solution has the potential to impact the patient/physician trust dynamic (14). From data privacy and security, through potential misuse to an augmented balance of shared accountability and risk, use of AI in practice alters the patient physician dynamic.…”

Section: Resultsmentioning

confidence: 99%