Artificial intelligence in wearable biosensing: Enhancing data analysis and decision-making.

The convergence of artificial intelligence (AI) and wearable biosensors is revolutionizing personalized healthcare, enabling continuous monitoring, early detection of health issues, which enhances the efficiency of data processing and real-time decision-making. Multimodal Large Language Models (MLLMs) play a pivotal role in this ecosystem by offering advanced capabilities in analyzing complex health data, understanding nuanced health contexts, and generating tailored health recommendations instantaneously. This study provides insights into how machine learning, deep learning algorithms, and MLLM can work together to facilitate the analysis of physiologic data for real-time monitoring and early warning systems as well as complex decision support mechanisms. In addition, the emergence of clinical decision support systems (CDSS) driven by AI and MLLM provides comprehensive recommendations. Looking ahead, the potential convergence of digital people, meta-universes and world models with wearable biosensors presents an innovative vision for personalized health management.