Artificial intelligence

AI in Diabetes Management Market: Age of Data-driven Precision Care is here

AI in Diabetes Management Market

Hopes of Better Accuracy, Efficiency, and Ease of Use in Diabetes Care Shape Paradigm of Artificial Intelligence (AI) in Diabetes Management—Improving Clinical Decision Making

Diabetes and diabetes-related complications are a huge burden on worldwide population—pooling 12% health expenditures. Sadly, despite the diabetes making vast stride, undiagnosed and untreated diabetes mellitus defeats the purpose of fighting against the rising global pandemic. Courtesy this: 1 of 2 adults with diabetes are undiagnosed while artificial intelligence (AI) has emerged as a tall hope for attaining the goal of optimal care for persons with diabetes. Evidently, machine learning and other AI-powered devices have attracted market prospects with their transformative potential in all aspects of diabetes care. Informed choices across the entire spectrum—patients, primary care clinicians, endocrinologists, health professionals, family, and caregivers are essential to maintaining lifelong care.

AI Platforms Empowering Patients and Care Providers to Fight against the Global Pandemic

The range of aspects that AI can touch on is bewildering, unarguably. Pooling genomics and exogenous data, and epigenetic alterations with the convolutional neural networks models could help know the genetic susceptibility to this diabetes.

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A number of AI-enabled projects by private players in China, India, the U.K., and the U.S. pool dizzying volume of information are testimony to the potential of AI in promoting patient-oriented programs. These programs can straddle on several touchpoints. Resultantly, AI programs help patients in timely detection and early treatment; this is a key aspect of management of diabetes and its complications. Additionally, they help in continuous monitoring and tracking of patients symptoms. A recent program is efforts made by Rensselaer Polytechnic Institute in 2019 is notable. The researchers aimed at improving the effectiveness of AI platforms—insulin pumps, for example–in managing Type 1 diabetes; they are aiming to allow AI-backed mobile health tools in early detection.

High Prevalence Rates Kick Off AI and ML Programs

A prominent example is the use of wearables in reducing the morbidity. By 2045-end, per the International Diabetes Federation (IDF), the diabetic population is projected to touch 693 million. Another common application is use AI in automated retinal screening. AI platforms are being used in pooling EHR data in ophthalmology centers in India. The country market with vast latent opportunities for AI/ML developers.

The country has prevalence rate of 8–10, spurring on research in AI-powered tools. In other countries as well, a number of AI-powered tools have been approved by regulatory agencies.

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Patient Self-Management a Huge Avenue

The adoption of AI-backed mobile health tools continue to proliferate. To put the trend in perspective, a 2017 survey revealed that 68% of mobile health app developers had diabetes management on the top of their minds. This underlines the rapid inroads AI is expected to continue making in transforming diabetes care. Broadly, key aspects where AI in diabetes management market see vast horizon are clinical decision support, predicting risk stratification in various population, and patient self-management.

Two of the most prominent examples that endow players in the AI in diabetes management market are patient self-management tools and lifestyle guidance for diabetics. The latter is an integral part of automated AI support. The market propositions of both are colossal, evidently drawing the attention of developers of machine learning algorithms and apps.

The management of Type 2 patients pose a tough challenge for healthcare professionals. The risk for type 2 patients are fuelled by obesity epidemic and a sedentary lifestyle. The cohort is thus a huge avenue for digital health device manufacturers in AI in diabetes management market.

AI technologies have shifted their attention on two aspects of diabetes education: improving the efficiency of prediction and in empowering patients to undertake lifestyle intervention. The growing number of studies, notably in China, on delivering AI-enabled assessment model for postprandial hyperglycaemia. These models will help identify high-risk patient who need to take oral glucose tolerance test (OGTT). Such examples point to the rising potential in AI in diabetes management market.

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