**3.5 Endocrine**

Diabetes affects millions of people around the world, accounts for approximately 12% of global health expenditures, and still one in two persons continue to be unaware they have the disease and are sub-optimally treated [25]. Early intensive mediation may prevent onset and decelerates the development of retinopathy, nephropathy, neuropathy, and other difficulties associated with diabetes [26]. Lack of timely, crucial health data is vital for the patient and provider to make well-educated decisions in regards to diabetics care. AI may provide timely information concerning a diabetic patient's health. A review of literature shows that the relationship between AI and diabetes management can be group into four categories that include automated retinal screening that was discussed above, clinical decision support, predictive population risk stratification, and patient self-management support tools [27].

AI-driven extrapolative modeling proactively recognizes diabetics with the greatest risk for needless complications that create avoidable emergency department outings, hospital stays, and readmissions [28]. AI can dig through various patient information to classify and describe diabetes populations [29]. In addition, patients with risk factors for diabetic comorbid conditions may be discovered [30–32]. AI may pinpoint individuals who may benefit from specific diabetes disease management programs [32]. On a molecular level it may aid in the discovery of proteins and genes linked with diabetes [33, 34].

AI can run practice decision-support instruments to aid healthcare professionals tailor diabetes treatments that boosts compliance and maximizes outcomes on a population level [35]. AI-powered devices may even diagnose diabetes noninvasively [36]. Furthermore diabetic neuropathy and diabetic wounds may be more accurately measured and treated [37, 38].

There is ongoing research on a Closed Loop System, which is a synthetic pancreas that blends continuous glucose measurement and an algorithm-run insulin pump to enhance diabetes self-management and lower hypoglycemic episodes [39]. A metareview of 12 trials compared patient acceptance of Artificial Pancreas Devices (APDs) versus standard of care. Based on the results, the authors surmised that the latest APD were safe and demonstrated high patient satisfaction [40].

More investigations are being done to determine the potential of diabetes apps to support persons in tracing and examining their statistics easily and to convey custommade evidence-based understandings that diabetic patients may employ every day. For example, all-inclusive dietary databanks can describe nutritional subject matter once a barcode is scanned on a smart device, explore food chain options, common food items, or distinguish food stuffs [41]. Machine Learning and representative analysis can diagnose and enumerate complex happenings and the standard of living of diabetic patients and provide assistance so they are better informed about the decisions they make [42]. AI may possibly quicken wound recovery, avoid unnecessary expenses secondary to commutes, and lower medical expenditures with the use of an AI-based smartphone camera [43]. Pregnant women with gestational diabetes have demonstrated approval of AI supplemented telemedicine appointments to help expedite clinical care via the amalgamation of AI interpreted evidence-based procedures, information obtained from EMR's, and blood sugars, blood pressure readings, and movement sensors [44].

## *AI in Healthcare: Implications for Family Medicine and Primary Care DOI: http://dx.doi.org/10.5772/intechopen.111498*

In 2018 Medtronic's Guardian Connect, the first AI-powered continuous glucose monitoring (CGM) system, was approved by the FDA in service of diabetic patients between the ages of 14 and 75 years. A prognostic system signals patients of substantial oscillation in glycaemia up to an hour before the critical event happens. The system has demonstrated accuracy and has been shown to announce around 98.5% of hypoglycemic occurrences; consequently, patients could potentially seize control to stabilize blood sugar [27]. The records can be collectively distributed and supervised by all relevant stakeholders involved in the patient's care.

Several questions persist before technological advancements in diabetes care permeate the health care sector. Practical interoperability, the capacity of two or more structures to interchange and utilize data, remains an obstacle [45]. Cost, overhead, continued expenditures, buy-in from healthcare providers and relevant participants, and the various definitions and involvedness surrounding the term Meaningful Use are all additional barriers to implementation [46]. The ability to replicate outcomes from previous studies remains blurry as well. For various reasons proprietary data such as source codes may be difficult to share. For example A survey of approximately 400 algorithms presented in papers at an Artificial Intelligence conference revealed around 6% of the presenters disseminated an organization's code, a third distributed information utilized to tryout their algorithms, and half provided an abridged of a source code (pseudo-code) [27]. Even if some of this data can be obtained it remains to be seen if the results will end up the same. What's more in machine learning, which stems from mastery of previous encounters, may be influenced by the typology of speech patterns implemented.

Nevertheless, diabetes remains an attractive target from AI research to apply industrial methods to solve the various complexities surrounding this disease. Many technological products have obtained approval from the FDA, are on the market, and have shown promising results. More innovative approaches are being created to challenge the status quo of current diabetic care by the enhancement of reliability, effectiveness, operability, straightforwardness, and patient, family, and provider, satisfaction with applying these products for diabetic management. Ideally, the right mix of monitoring and appropriate feedback will help isolate telling precedents and head to customized understandings that boost patient and provider commitment, conviction, and achievement in optimizing blood sugar control.
