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Improving Clinical Diagnostics and Patient Care through Artificial Intelligence and Biosensor Technologies.

Sylwia Baluta1, Vishnu Suresh2, Milena Chmielowska3

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Artificial Intelligence (AI) and machine learning (ML) significantly enhance biosensor precision for medical diagnosis and continuous monitoring. Challenges like data quality and ethical concerns are addressed to advance AI-powered personalized medicine.

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Biosensor Technology

Background:

  • Biosensors are crucial for detecting physiological signals in medical diagnosis.
  • Advancements in Artificial Intelligence (AI) and machine learning (ML) offer potential improvements in biosensor efficacy and precision.
  • Current biosensor applications include early disease detection and continuous patient monitoring.

Purpose of the Study:

  • To analyze the advantages of AI and ML in biosensor technology.
  • To highlight contemporary developments and applications of biosensors in medical diagnosis.
  • To address challenges and ethical considerations hindering AI-enhanced biosensing.

Main Methods:

  • Perspective analysis of AI and ML integration in biosensors.
  • Review of current biosensor technology and medical diagnostic applications.
  • Discussion of barriers to AI adoption and ethical/legal implications.

Main Results:

  • AI and ML substantially improve the accuracy and precision of biosensor-based physiological signal detection.
  • AI-enhanced biosensors show promise for early disease detection and continuous monitoring.
  • Key challenges include data quality, AI technique relevance, data privacy, and legal issues.

Conclusions:

  • AI-enhanced biosensing systems have the potential to significantly improve healthcare outcomes.
  • Overcoming current challenges can facilitate the widespread adoption of AI in personalized medicine.
  • Further research into innovative approaches is needed to fully realize the potential of AI in biosensing.