IntroductionMedical artificial intelligence (AI) is moving forward at considerable pace. Promising research ideas are surfacing in clinical waters; AI is automating the national 111 triage service and has exhibited dermatologist‐level performance at identifying suspicious skin lesions, a task where experts frequently disagree . The present article explores how machine learning, a prominent branch of AI, may be set to transform diabetes care.What is machine learning?Machine learning finds relationships within data; for example, a linear regression proposes a ‘line of best fit’ relationship. Such simple representations may struggle, however, to describe more complex data, as shown in Fig. . When clinical guidelines are based on simple models, they might fail to make allowances for the enormous variability found in a person's genome, medical history and their behaviours. By contrast, AI research has developed machine learning techniques that can model very large datasets with multiple, non‐linear relationships. For example, a ‘neural network’ represents data as vast numbers of interconnected neurons, analogous to the human brain; therefore, such models can approach clinical problems in a similar fashion to a clinician by integrating numerous sources of disparate information and providing a personalized solution.(a) and (b) Machine learning reveals important relationships in complex data: an
Diabetic Medicine – Wiley
Published: Jan 1, 2018
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