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Artificial Intelligence-Based Digital Biomarkers for Type 2 Diabetes: A Review
Mariam Jabara1, Orhun Kose2, George Perlman2
1Centre for Outcome Research & Evaluation, McGill University Health Centre, Montréal, Québec, Canada; Division of Experimental Medicine, Faculty of Medicine and Health Science, McGill University, Montréal, Québec, Canada.
The Canadian Journal of Cardiology
|August 7, 2024
Summary
Artificial intelligence (AI) and digital biomarkers show promise for revolutionizing type 2 diabetes mellitus (T2DM) screening and management. These technologies offer new avenues for early detection, complication diagnosis, and personalized patient care.
Area of Science:
- Digital health technologies
- Artificial intelligence (AI)
- Biomarker development
Background:
- Type 2 diabetes mellitus (T2DM) is a significant global health burden requiring early detection and effective management.
- Existing healthcare systems face challenges in timely T2DM diagnosis and complication management.
- Digital health innovations are emerging as potential solutions for improving T2DM care.
Purpose of the Study:
- To review the applications of AI-driven digital biomarkers in T2DM screening, complication diagnosis, and patient management.
- To discuss the benefits of multisensor devices for digital biomarker development in T2DM.
- To highlight AI's role in clinical interventions and implementation strategies for T2DM.
Main Methods:
- Review of current literature on AI-driven digital biomarkers for T2DM.
- Analysis of multisensor device data for digital biomarker generation.
- Examination of AI techniques in clinical decision support, telemedicine, and population health for T2DM.
Main Results:
- AI-driven digital biomarkers demonstrate potential in enhancing T2DM screening and diagnosis of complications.
- Multisensor devices integrated with AI can yield valuable digital biomarkers for T2DM management.
- AI facilitates clinical interventions, including decision support systems and telemedicine, for T2DM patients.
Conclusions:
- AI-powered digital biomarkers offer a transformative approach to T2DM screening, diagnosis, and management.
- Addressing challenges like data privacy, algorithm interpretability, and regulatory hurdles is crucial for successful implementation.
- Future research should focus on validating and integrating AI-driven digital biomarkers into routine T2DM care.
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