Prime Highlights
- AI-based glucose monitors provide real-time metabolic information but typically match traditional monitors for accuracy.
- For non-diabetics, CGMs can help track health habits, though their necessity and benefits remain disputed.
Key Facts
- AI-based CGMs tend to overestimate blood glucose values by about 0.9 mmol/L compared with finger-prick testing.
- They offer personalized advice according to diet, exercise, and sleep data.
- Health professionals caution that CGMs are of limited use for healthy individuals with no metabolic condition.
Key Background
Continuous Glucose Monitors (CGMs) originally developed to help diabetics track their blood sugar levels in real time, have been used over the years by health enthusiasts and biohackers. They track glucose levels within interstitial fluid via a tiny sensor implanted under the skin. They send data to an accompanying smartphone app that graphs glucose trends throughout the day.
Over the last two years, newer CGMs have come with artificial intelligence to enhance their abilities further. The AI trackers review the consumer’s eating, sleeping, and exercise patterns as well as their glucose levels. They seek to offer actionable recommendations—such as telling a user to take a walk after having a high-carb meal or to improve sleeping habits if blood sugar does not come down overnight.
During a two-week comparison trial, the AI-glucose monitor was as good as a regular finger-prick glucose monitor in nearly all situations. But it was noted that the CGM tended to overestimate fasting and post-meal glucose levels. It overread by 0.9 mmol/L on average compared to the traditional device. It also reported longer durations of high glucose levels after meals, which experts attribute to being able to mislead users unless properly interpreted.
Physicians have differing opinions regarding the general public’s use of CGMs without diabetes. Some think that such devices will promote more awareness about health and lead to healthier eating and lifestyle choices, whereas others caution that a temporary spike in blood glucose is normal and not inherently problematic. In addition, the information can be burdensome or distressing for those without a medical requirement to constantly track glucose levels.
For those who are susceptible to Type 2 diabetes or who have prediabetes, AI-powered CGMs can potentially help early on by identifying glucose trends that suggest metabolic problems. But for healthy people, the benefit could be counterbalanced by the cost or disinformation.
In short, AI-powered CGMs give us a glimpse of the future of personal health monitoring. Feature-rich and precise in detecting patterns, they are now more suitable as awareness devices than as medical necessities—unless you have a specific medical condition.
