An awakening in medicine: the partnership of humanity and intelligent machines

Celi, Leo Anthony; Fine, Benjamin; Stone, David J.

doi:10.1016/s2589-7500(19)30127-x

Cited by 41 publications

(29 citation statements)

References 10 publications

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“…Programs and events bring together clinicians, computer scientists, and engineers to create a collaborative ecosystem that can leverage the power of data science [42]. These crossdisciplinary partnerships are crucial in order to design ML in a manner that is usable, safe, and trustworthy for all the humans touched by its applications [43].…”

Section: Discussion: Challenges and Next Stepsmentioning

confidence: 99%

Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review

2020

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Machine learning (ML) is a discipline of computer science in which statistical methods are applied to data in order to classify, predict, or optimize, based on previously observed data. Pulmonary and critical care medicine have seen a surge in the application of this methodology, potentially delivering improvements in our ability to diagnose, treat, and better understand a multitude of disease states. Here we review the literature and provide a detailed overview of the recent advances in ML as applied to these areas of medicine. In addition, we discuss both the significant benefits of this work as well as the challenges in the implementation and acceptance of this non-traditional methodology for clinical purposes.

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Section: Discussion: Challenges and Next Stepsmentioning

confidence: 99%

Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review

2020

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“…As data continue to emerge about poor usability and workflow integration of health IT and adverse patient safety consequences,64 it is essential to integrate HF methods and principles in technology design processes 11 65–68. This is particularly important with the emergence of artificial intelligence, which can be used to design CDS technologies that complement and enhance clinical decision-making 69–71…”

Section: Discussionmentioning

confidence: 99%

Application of human factors to improve usability of clinical decision support for diagnostic decision-making: a scenario-based simulation study

et al. 2019

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ObjectiveIn this study, we used human factors (HF) methods and principles to design a clinical decision support (CDS) that provides cognitive support to the pulmonary embolism (PE) diagnostic decision-making process in the emergency department. We hypothesised that the application of HF methods and principles will produce a more usable CDS that improves PE diagnostic decision-making, in particular decision about appropriate clinical pathway.Materials and methodsWe conducted a scenario-based simulation study to compare a HF-based CDS (the so-called CDS for PE diagnosis (PE-Dx CDS)) with a web-based CDS (MDCalc); 32 emergency physicians performed various tasks using both CDS. PE-Dx integrated HF design principles such as automating information acquisition and analysis, and minimising workload. We assessed all three dimensions of usability using both objective and subjective measures: effectiveness (eg, appropriate decision regarding the PE diagnostic pathway), efficiency (eg, time spent, perceived workload) and satisfaction (perceived usability of CDS).ResultsEmergency physicians made more appropriate diagnostic decisions (94% with PE-Dx; 84% with web-based CDS; p<0.01) and performed experimental tasks faster with the PE-Dx CDS (on average 96 s per scenario with PE-Dx; 117 s with web-based CDS; p<0.001). They also reported lower workload (p<0.001) and higher satisfaction (p<0.001) with PE-Dx.ConclusionsThis simulation study shows that HF methods and principles can improve usability of CDS and diagnostic decision-making. Aspects of the HF-based CDS that provided cognitive support to emergency physicians and improved diagnostic performance included automation of information acquisition (eg, auto-populating risk scoring algorithms), minimisation of workload and support of decision selection (eg, recommending a clinical pathway). These HF design principles can be applied to the design of other CDS technologies to improve diagnostic safety.

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“…More suggestions for improving the quality of research on supervised machine learning can be found in the paper by Cearns et al (2019). Celi et al (2019) describe the future in a very brief essay that is worth quoting:…”

Section: Integrating Ai and The Mental Health Services Infrastructurementioning

confidence: 99%

Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health

Bickman

2020

Adm Policy Ment Health

111

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This conceptual paper describes the current state of mental health services, identifies critical problems, and suggests how to solve them. I focus on the potential contributions of artificial intelligence and precision mental health to improving mental health services. Toward that end, I draw upon my own research, which has changed over the last half century, to highlight the need to transform the way we conduct mental health services research. I identify exemplars from the emerging literature on artificial intelligence and precision approaches to treatment in which there is an attempt to personalize or fit the treatment to the client in order to produce more effective interventions. Keywords Mental health services • Artificial intelligence • Machine learning • Precision mental health • Randomized clinical trials (RCTs) • Precision medicine "AI will bring many wonders. It may also destabilize everything from nuclear détente to human friendships. We need to think much harder about how to adapt... it is changing human knowledge, perception and reality-and, in so doing, changing the course of human history. We seek to understand it and its consequences and encourage others across the disciplines to do the same."-Kissinger et al. 2019, pp. 24-26 "AI is one of the most profound things we're working on as humanity. It's more profound than fire or electricity."-Alphabet CEO Sundar Pichai, in Thompson and Bodoni 2020. This article is part of a special issue, Festschrift for Leonard Bickman: The Future of Children's Mental Health Services.

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An awakening in medicine: the partnership of humanity and intelligent machines

Cited by 41 publications

References 10 publications

Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review

Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review

Application of human factors to improve usability of clinical decision support for diagnostic decision-making: a scenario-based simulation study

Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health

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