AI-driven diabetes prevention matches human-led programs in clinical trial
In a randomized trial, an AI-powered lifestyle app helped people with prediabetes meet CDC risk-reduction benchmarks at rates similar to human-led programs, while boosting initiation and completion.
Researchers at Johns Hopkins Medicine and the Bloomberg School of Public Health report that an AI-driven diabetes prevention program achieved outcomes comparable to traditional, human-led coaching. The results come from a phase III randomised controlled trial, the first of its kind.
The trial enrolled participants with prediabetes and randomly assigned them to one of four remote human-led programs or an AI app that delivered personalised push notifications guiding diet, exercise and weight management. Over 12 months, both groups were evaluated against CDC benchmarks for risk reduction (e.g. achieving 5 % weight loss, meeting activity goals, or reducing A1C).
After one year, 31.7 % of AI-app users and 31.9 % of human-led participants met the composite benchmark. Interestingly, the AI arm saw higher initiation rates (93.4 % vs 82.7 %) and completion (63.9 % vs 50.3 %) than human programs.
The researchers note that scheduling, staffing, and access barriers can limit traditional lifestyle programs. The AI approach, which runs asynchronously and is always available, may help expand reach, especially for underserved populations or when human resources are constrained.
Future work will assess how these findings scale in broader, real-world patient groups and explore cost effectiveness, user preferences and the balance between AI and human support.
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