Transforming Diabetes Care Through Artificial Intelligence: The Future Is Here

Abstract

An estimated 425 million people globally have diabetes, accounting for 12% of the world's health expenditures, and yet 1 in 2 persons remain undiagnosed and untreated. Applications of artificial intelligence (AI) and cognitive computing offer promise in diabetes care. The purpose of this article is to better understand what AI advances may be relevant today to persons with diabetes (PWDs), their clinicians, family, and caregivers. The authors conducted a predefined, online PubMed search of publicly available sources of information from 2009 onward using the search terms “diabetes” and “artificial intelligence.” The study included clinically-relevant, high-impact articles, and excluded articles whose purpose was technical in nature. A total of 450 published diabetes and AI articles met the inclusion criteria. The studies represent a diverse and complex set of innovative approaches that aim to transform diabetes care in 4 main areas: automated retinal screening, clinical decision support, predictive population risk stratification, and patient self-management tools. Many of these new AI-powered retinal imaging systems, predictive modeling programs, glucose sensors, insulin pumps, smartphone applications, and other decision-support aids are on the market today with more on the way. AI applications have the potential to transform diabetes care and help millions of PWDs to achieve better blood glucose control, reduce hypoglycemic episodes, and reduce diabetes comorbidities and complications. AI applications offer greater accuracy, efficiency, ease of use, and satisfaction for PWDs, their clinicians, family, and caregivers.

Delivering high‐tech, AI‐based health care at Apollo Hospitals

“…Mobile phone‐based AI apps are being developed for diagnosing diabetes by analyzing images of a person's retina. These apps can also be used to suggest treatment by analyzing genetic factors (Dankwa‐Mullan et al, ). For example, the DreaMed diabetes management platform uses cloud‐based artificial intelligence to offer personalized monitoring and treatment plans.…”

Use of Artificial Intelligence to Improve Diabetes Outcomes in Patients Using Multiple Daily Injections Therapy

“…Work is being done on physician-focused ADS systems to help providers identify whom to screen for T2D, how to optimize complication reduction and guideline compliance with routine studies for patients with T1D and T2D, and automated reading of images screening for diabetic retinopathy. [19][20][21][22] Although these frontiers are exciting, they are more focused on provider efficiency rather than patient-centered application of ADS technologies. To date, there are limited clinical trial data on the use of ADS for MDI patients.…”

View recommendation for visual data exploration

“…It facilitates the user in formulating input, for instance, recent interface allow users to provide input in terms of keywords, uncertain queries, example tuples, graphical parameters and the output can be displayed in the form of interesting visualizations which aid steps of the result review and reformulation of the input for the next iteration. Key requirements of the exploratory interface is that it should be simple enough to avoid complicate declarative languages and, at the same time, it should have flexibility and expressiveness to satisfy complex information needs [24]. Generally, exploratory interfaces can be divided into the following three categories [21]:…”

“…1. Example-driven exploration: Generally in the data-driven exploration, the user is unable to express their data interests precisely, but she may have an idea of what an interesting result will look like [24][25][26][27][28]. In such situations, the user can be aided with an interface to navigate through subsets of data to find interesting insights.…”

“…For diseases like diabetes with high global occurrence, it has been highlighted that there will not be enough family care doctors to meet future needs [102]. Any allied healthcare professionals or carers as physician assistants or family members, will likely take that role, but they will need easy-to-understand visual information tools that give them quick yet accurate insights [24]. Therefore taking a case of using a diabetes dataset, the proposed techniques in this research demonstrate how user-centred visualizations simplify the interpretation process by presenting insights.…”