We present a structured overview of methods, challenges, and solutions, aiming to support researchers and practitioners in developing reliable and explainable AI solutions for digital health. This paper is further enriched with detailed discussions of the contributions toward robustness and explainability in digital health, the development of trustworthy AI systems in the era of LLMs, and various evaluation metrics for measuring trust and related parameters such as validity, fidelity, and diversity.
A comprehensive review of AI-powered wearable biosensors emphasizing how machine learning and edge AI enable real-time health monitoring and personalized care, with insights on digital twins, LLMs, and challenges in privacy, scalability, and clinical integration.
We developed GlucoLens, that takes sensor-driven inputs and uses advanced data processing, large language models, and explainable machine learning models to predict postprandial AUC and hyperglycemia from diet, physical activity, and recent glucose patterns.
A privacy-preserving system that leverages Gramian Angular Field (GAF) transformations, Federated Learning, and wearable sensor data to detect Freezing of Gait (FoG) in individuals with Parkinson’s Disease
We introduce MetaBoost, a novel hybrid framework that integrates SMOTE, ADASYN, and CTGAN, optimizing synthetic data generation through weighted averaging and iterative weight tuning to enhance the model's performance (achieving a 1.87% accuracy improvement over individual balancing techniques).
By integrating BERT-based word embeddings with domain-specific knowledge (i.e., MET values), FUSE-MET optimizes label merging, reducing label complexity and improving classification accuracy.
We propose an automated risk prediction system that can make recommendations to clinicians in real-time with machine learning classifiers that predict the risk of developing neurological impairment.
We focus on devising algorithms that combine data about physical activity and engagement with the app to predict future physical activity performance.
We propose an algorithm that can reconstruct the missing input data for unavailable sensors.