The Future of Diabetes Management: A Review of the Latest Advances in Artificial Intelligence

The Future of Diabetes Management: A Review of the Latest Advances in Artificial Intelligence

Artificial intelligence has already had a remarkable effect in multiple industries. From finance and national security, health care, criminal justice, transportation and beyond – AI systems are revolutionizing decision making, business models, risk mitigation strategies and system performance for end users and organizations alike.

Valbiotis recently reported positive clinical results for its TOTUM 63 diabetes treatment that targets microbiomes without immunosuppression, expected to have an enormously beneficial impact on treating diabetes in future.

Personalized Medicine

Since the 1990s, much focus has been given to developing treatments tailored specifically to individual patient profiles. From drug therapies that only work or have no side effects for certain genetic profiles to customized screening tools that allow early identification, the goal is to reduce overall disease progression risk while aiding early diagnosis.

Modelling and simulating disease trajectories bears striking resemblances to attempts in other disciplines like climate science, ecology, economics or social systems. Disease models tend to be highly nonlinear with redundant feedback relations intertwining like webs of spider webs resulting in critical transitions – analogous to tipping points – occurring throughout system evolution.

Pharmacogenomics (PGx), in which drug treatment is tailored to an individual’s genetic profile based on susceptibility or response to particular medication, has provided exciting advances. Yet according to an article in Science magazine, translation between genetic association studies and clinical realities remains extremely challenging.

Artificial Intelligence (AI)

Artificial Intelligence (AI), commonly referred to as machine learning, is an area of computer science which deals with developing algorithms capable of analyzing vast quantities of data and making advanced inferences from them. AI has quickly become a staple part of technology industry and has made significant strides toward diabetes management.

AI can enable patients to receive tailored notifications to enhance their care. Teladoc Health developed a mobile app that uses AI to detect trends in blood sugar levels, then suggest lifestyle changes to keep them from worsening further. Teladoc claims the app can also serve as an invaluable resource for people living with Type 1 diabetes who cannot regulate their own glucose levels – those unable to regulate themselves could develop serious complications including nerve damage or even coma as a result of not managing it effectively themselves.

Other applications of AI in diabetes care include predicting new-onset diabetes and risk stratification, though machine learning (ML) models don’t consistently outperform traditional statistical prediction methods for this purpose and often result in overfitting in training populations.

Robotics

Robotics offers promise for improving diabetes management among children, particularly through social robotics. Utilizing robots can teach children how to take control of their condition while providing relief from anxiety brought on by having this disease.

Recently, a study utilized an NAO robot to deliver an engaging talk-based program and coach children living with Type 1 diabetes on how best to manage their condition. An eight-week feasibility trial confirmed its efficacy while showing participants improved their perceived self-efficacy levels.

This research marks a groundbreaking first: using a cognitively and motivationally autonomous affective robot toddler to support children’s perceptions of self-efficacy, autonomy and emotional wellbeing in a situated human-robot partnership for daily diabetes care. The partnership features an adaptive interaction model adapted to suit children’s individual needs and preferences – opening the way to the creation of human agent/robot teams with collective intelligence capabilities.

Self-Management

Self-management refers to an active approach by individuals living with diabetes in managing their own illness. This may involve lifestyle choices, medication adherence and blood glucose monitoring to achieve improved glycemic control and lower the risk of long-term complications.

Literature on health-related self-management supports the theory that patients’ confidence in their ability to perform certain behaviors influences actual performance (3-6). Yet little research has explored this phenomenon among low-income populations where limited health literacy may pose additional risk.

Researchers conducted semi-structured interviews with ten patients recently diagnosed with type 2 diabetes mellitus (T2DM) who received diabetes-related care in Dutch primary care. Results from these interviews demonstrated that respondents experiencing active self-management of T2DM at first seemed like part of their daily routine; over time though they no longer perceived themselves as actively managing it.

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