A Path for Translation of Machine Learning Products into Healthcare Delivery

Sendak, Mark; D’Arcy, Joshua; Kashyap, Sehj; Gao, Michael; Nichols, Marshall; Corey, Kristin; Ratliff, William; Balu, Suresh

doi:10.33590/emjinnov/19-00172

Cited by 36 publications

(17 citation statements)

References 74 publications

(49 reference statements)

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“…However, as the demand for A.I. to be implemented into everyday medicine is getting higher by patients, policy makers, medical professionals and hospitals, its way from developers to practice will have to become faster 25 . A typical example of how it has worked so far is related to Kardia, formerly known as AliveCor.…”

Section: Questionsmentioning

confidence: 99%

A short guide for medical professionals in the era of artificial intelligence

Meskó

Görög²

2020

npj Digit. Med.

249

201

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Artificial intelligence (A.I.) is expected to significantly influence the practice of medicine and the delivery of healthcare in the near future. While there are only a handful of practical examples for its medical use with enough evidence, hype and attention around the topic are significant. There are so many papers, conference talks, misleading news headlines and study interpretations that a short and visual guide any medical professional can refer back to in their professional life might be useful. For this, it is critical that physicians understand the basics of the technology so they can see beyond the hype, evaluate A.I.-based studies and clinical validation; as well as acknowledge the limitations and opportunities of A.I. This paper aims to serve as a short, visual and digestible repository of information and details every physician might need to know in the age of A.I. We describe the simple definition of A.I., its levels, its methods, the differences between the methods with medical examples, the potential benefits, dangers, challenges of A.I., as well as attempt to provide a futuristic vision about using it in an everyday medical practice.

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Section: Questionsmentioning

confidence: 99%

A short guide for medical professionals in the era of artificial intelligence

Meskó

Görög²

2020

npj Digit. Med.

249

201

View full text Add to dashboard Cite

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“…We disruptive AI-driven companies taking similar marketing-heavy paths [218,283,545,730]. [673,707,758,890], but with collaborative interest from sports scientists, the models developed e.g.…”

Section: Discussionmentioning

confidence: 99%

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

Teikari¹,

Pietrusz²

2021

Preprint

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In strength training, personalised strength training (autoregulation) approaches have been used to individualise exercise programs with monitoring an for dynamic adjustment based on their responses to training. While this transition from tradition-based training to evidence-based training framework has been an improvement in training practices, we argue that the future of strength training will also incorporate deep learning models powered by data. We refer to this data-driven framework as precision strength training inspired by the similar modeling frameworks used in precision medicine. In contrast to current personalised training in which the acquired athlete data is often subject to human expert decision-making, we are anticipating the rise of human-in-the-loop systems with an augmented coach who will be doing decisions collaboratively with the machine. Similar to other precision frameworks, such as precision health, we envision such a future to take decades to be realised and we focus here on practical short-term targets on a way to long-term realisation. In this chapter, we will review the measurement technology needed for continuous data acquisition from an individual during training/physical activity, how to acquire these datasets for the development of such systems and, how a proof-of-concept system could be developed for powerlifting training with applicability to general strength and conditioning (S&C) and physical rehabilitation purposes. Additionally, we will evaluate how the user experience (UX) of the system feedback and visualisation could be designed.

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“…Despite robust interest, machine learning models have rarely been translated into clinical care [21]. In recent years, there has been a proliferation of suicide risk prediction models [11,13,[22][23][24], but the implementation of these models has been much more limited.…”

Section: Principal Findingsmentioning

confidence: 99%

Designing a Clinical Decision Support Tool That Leverages Machine Learning for Suicide Risk Prediction: Development Study in Partnership With Native American Care Providers

Haroz¹,

Grubin²,

Goklish³

et al. 2021

JMIR Public Health Surveill

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Background Machine learning algorithms for suicide risk prediction have been developed with notable improvements in accuracy. Implementing these algorithms to enhance clinical care and reduce suicide has not been well studied. Objective This study aims to design a clinical decision support tool and appropriate care pathways for community-based suicide surveillance and case management systems operating on Native American reservations. Methods Participants included Native American case managers and supervisors (N=9) who worked on suicide surveillance and case management programs on 2 Native American reservations. We used in-depth interviews to understand how case managers think about and respond to suicide risk. The results from interviews informed a draft clinical decision support tool, which was then reviewed with supervisors and combined with appropriate care pathways. Results Case managers reported acceptance of risk flags based on a predictive algorithm in their surveillance system tools, particularly if the information was available in a timely manner and used in conjunction with their clinical judgment. Implementation of risk flags needed to be programmed on a dichotomous basis, so the algorithm could produce output indicating high versus low risk. To dichotomize the continuous predicted probabilities, we developed a cutoff point that favored specificity, with the understanding that case managers’ clinical judgment would help increase sensitivity. Conclusions Suicide risk prediction algorithms show promise, but implementation to guide clinical care remains relatively elusive. Our study demonstrates the utility of working with partners to develop and guide the operationalization of risk prediction algorithms to enhance clinical care in a community setting.

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A Path for Translation of Machine Learning Products into Healthcare Delivery

Cited by 36 publications

References 74 publications

A short guide for medical professionals in the era of artificial intelligence

A short guide for medical professionals in the era of artificial intelligence

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

Designing a Clinical Decision Support Tool That Leverages Machine Learning for Suicide Risk Prediction: Development Study in Partnership With Native American Care Providers

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