Artificial intelligence (AI) and its implementations such as Machine Learning (ML) are promising major breakthroughs in diabetes treatment

  • Hannah
  • February 21, 2021
  • Comments Off on Artificial intelligence (AI) and its implementations such as Machine Learning (ML) are promising major breakthroughs in diabetes treatment

Artificial intelligence (AI) attempts to emulate the cognitive functions of humans. It brings a fundamental shift to healthcare, driven by growing healthcare data availability and rapid development of analytical methods. This involves methods of treatment and their consequences, health outcomes, and pace of care collected through millions of patients, geographical areas, and myriad health conditions that are often intertwined. New computational power can detect and classify large and small data patterns and even find patterns to identify possible healthcare outcomes through machine learning.

According to Crayon, “Over 400 million people have diabetes but as many as 46% remain completely undiagnosed and untreated. In fact, diabetes accounts for 12% of the world’s health expenditure through the vast amount of research, analysis, and treatments, conducted and required today.”

To combat this, Artificial intelligence (AI) and its implementations such as Machine Learning (ML) are promising major breakthroughs in diabetes treatment. AI is making important strides in the field for those with diabetes, those who remain untreated, their relatives, and clinicians. Three key areas of diabetes treatment can be affected and improved by AI: diabetes patients, health care providers, and healthcare services. For patients with diabetes, AI has introduced new dimensions of self-management, implemented fast and efficient decision-making and efficient follow-up for health care providers, and improved usage of resources in health care systems.

Healthline mentioned that, today, companies like Livongo, Cecelia Health, One Drop, Virta Health, and mySugr are all up and running with AI-powered systems designed to help gather, store, disseminate, and utilize data for more efficient and individualized diabetes care.

Livongo, for example, combines blood sugar monitoring with coaching and remote monitoring (nudging the user when need be), along with some nice touches like keeping track of how many strips you use and reminding you to order. One Drop helps users track glucose levels along with activity, medications, and food, offers in-app coaching, and connects users to a community for support when needed. Virta Health offers virtual nutrition coaching for those with pre-diabetes and type 2 diabetes.

Certain ways of using AI to regulate diabetes:

Self-management of Diabetes

Self-management is the key to diabetic treatment. Patients are now encouraged to self-manage their disease, thanks to AI, utilizing personal data to adjust their lifestyle and function effectively as an at-home doctor.

Artificial intelligence helps patients to assess what to consume, as well as the required amount of exercise. And, through features such as real-time analysis of the high calorie value of food, mobile apps, like Suguard, make self-management much simpler.

Diabetes Early Detection

Early diagnosis of diabetes is critical for ensuring prompt care and helping individuals with diabetes live long and healthy lives. The measures of early detection are focused on both hereditary and behavioral trends in patients and the use of artificial intelligence is increasing every year in this field.

Whisk mentioned that, a device using artificial intelligence has been developed by doctors at a hospital in Shanghai that can help them classify patients at risk of developing type 2 diabetes – 3, 5 or maybe 15 years from this point. Their innovation is focused on trends of gender, height & weight, blood sugar, drinking & smoking record in patients.

Effective data collection for patients upgrades personalization

Artificial intelligence also makes it easier for patients to provide and access information, thus enhancing patient-physician engagement and encouraging more personalized treatment.

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