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Issues And Trends In Healthcare Delivery System01:29

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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Artificial intelligence in wearable biosensing: Enhancing data analysis and decision-making.

Zenghui Ding1, Wenhui Fang2, Jixue Zhang2

  • 1Institute of Intelligent Machines, Chinese Academy of Science, Hefei, Anhui, P.R. China.

Progress in Molecular Biology and Translational Science
|September 8, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and wearable biosensors are transforming personalized healthcare. Multimodal Large Language Models (MLLMs) enhance data analysis for real-time monitoring and decision support.

Keywords:
Artificial IntelligencePhysiological Data AnalysisWearable Biosensors

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence
  • Digital Health

Background:

  • Personalized healthcare is increasingly reliant on continuous data streams from wearable biosensors.
  • Artificial intelligence (AI) offers advanced capabilities for analyzing complex physiological data.
  • Multimodal Large Language Models (MLLMs) are emerging as powerful tools for interpreting nuanced health information.

Purpose of the Study:

  • To explore the synergistic integration of AI, machine learning, deep learning, and MLLMs with wearable biosensors.
  • To demonstrate the potential of these technologies in real-time physiological data analysis for early warning systems.
  • To highlight the role of AI and MLLMs in developing advanced clinical decision support systems (CDSS).

Main Methods:

  • Utilizing machine learning and deep learning algorithms for processing biosensor data.
  • Applying MLLMs for complex health data analysis and contextual understanding.
  • Developing AI-driven CDSS frameworks for generating health recommendations.

Main Results:

  • Demonstrated enhanced efficiency in data processing and real-time decision-making through AI integration.
  • Showcased MLLMs' capability in analyzing complex physiological data for early detection.
  • Illustrated the development of comprehensive recommendations via AI and MLLM-powered CDSS.

Conclusions:

  • The convergence of AI, MLLMs, and wearable biosensors significantly advances personalized healthcare.
  • Future integration with digital people and meta-universes promises innovative health management solutions.
  • AI-driven systems offer robust potential for real-time monitoring, early detection, and sophisticated clinical decision support.