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Artificial Intelligence for Noninvasive Health Diagnostics.

P R Wankhede1, Devendra Bhuyar1, Shrinivas Zanwar2

  • 1Department of Electronics and Computer Engineering, CSMSS Chh. Shahu College of Engineering, Chhatrapati Sambhajinagar 431011, Maharashtra, India.

ACS Sensors
|October 30, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) enhance noninvasive diagnostics by analyzing complex data for real-time monitoring. This review explores AI/ML applications, challenges, and future directions for improved healthcare diagnostics.

Keywords:
artificial intelligencebiosensorsclinical decisiondiagnosismachine learningnoninvasivesensorwearable

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Area of Science:

  • Biomedical Engineering
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Noninvasive diagnostics are crucial for early disease detection and patient care but often lack sensitivity and timely interpretation.
  • Artificial intelligence (AI) and machine learning (ML) offer solutions by identifying complex patterns in data, enabling continuous monitoring.

Purpose of the Study:

  • To review the integration of AI/ML across various noninvasive diagnostic platforms.
  • To highlight emerging AI/ML technologies and discuss barriers to their adoption in healthcare.

Main Methods:

  • Comprehensive literature review of AI/ML applications in noninvasive diagnostics.
  • Exploration of diverse platforms: medical imaging, wearable sensors, breath analysis, biofluid analysis, and optical sensing.
  • Discussion of advanced AI techniques like federated learning, explainable AI, digital twins, and nanosensors.

Main Results:

  • AI/ML integration is transforming diagnostics from ad hoc assessments to continuous, real-time monitoring.
  • Emerging directions include federated learning, explainable AI, digital twins, and nanosensors.
  • Significant barriers to adoption exist, including data privacy, algorithmic fairness, regulatory issues, and system integration.

Conclusions:

  • AI/ML holds immense promise for advancing noninvasive diagnostics, improving sensitivity, specificity, and accessibility.
  • Overcoming adoption barriers is critical for translating AI innovations into scalable, cost-effective, patient-centered healthcare solutions.
  • This review provides a roadmap for researchers, clinicians, and policymakers to navigate the future of AI-assisted noninvasive diagnostics.