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[Comment] Reporting of artificial intelligence prediction models

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Data-driven technologies that form the basis of the digital health-care revolution provide potentially important opportunities to deliver improvements in individual care and to advance innovation in medical research. Digital health technologies include mobile devices and health apps (m-health), e-health technology, and intelligent monitoring. Behind the digital health revolution are also methodological advancements using artificial intelligence and machine learning techniques. Artificial intelligence, which encompasses machine learning, is the scientific discipline that uses computer algorithms to learn from data, to help identify patterns in data, and make predictions. A key feature underpinning the excitement behind artificial intelligence and machine learning is their potential to analyse large and complex data structures to create prediction models that personalise and improve diagnosis, prognosis, monitoring, and administration of treatments, with the aim of improving individual health outcomes. Prediction models to support clinical decision making have existed for decades, and these include well known tools such as the Framingham Risk Score,
  • Wilson PW
  • D'Agostino RB
  • Levy D
  • Belanger AM
  • Silbershatz H
  • Kannel WB
Prediction of coronary heart disease using risk factor categories.

Circulation. 1998; 97 : 1837-1847