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Microbial biosensors are analytical devices that utilize living microbes to detect specific substances through measurable signals. These devices consist of two main components: biosensing organisms and signal-transducing elements. Biosensing organisms, such as Escherichia coli or Saccharomyces cerevisiae, are typically housed in multiwell plates connected to transducers, enabling rapid, real-time detection of target analytes.Signal Generation MechanismWhen a target analyte—such as...
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Artificial Intelligence-Powered Nanosensor Platforms for Non-Invasive Breathomic Diagnostics.

Vishal Chaudhary1,2, Pradeep Bhadola1

  • 1Centre for Theoretical Physics and Natural Philosophy, Nakhonsawan Studiorum for Advanced Studies, Mahidol University, Nakhonsawan, 60130, Thailand.

Nanotechnology, Science and Applications
|December 26, 2025
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Summary
This summary is machine-generated.

AI-powered nanosensors for breathomics diagnostics offer rapid, non-invasive disease detection. These advanced platforms show high accuracy for various conditions, paving the way for next-generation healthcare.

Keywords:
breathomic diagnosticscomplex healthcare systemsmachine intelligencenanomaterialssensors

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Area of Science:

  • Biomedical Engineering
  • Nanotechnology
  • Artificial Intelligence

Background:

  • Conventional diagnostics face limitations in cost, invasiveness, and accessibility.
  • A need exists for rapid, portable, and non-invasive health assessment tools.
  • AI-powered Nanosensors for Breathomics Diagnostics (AND) platforms offer a novel solution.

Purpose of the Study:

  • To review the progress of AND platforms in disease diagnostics.
  • To identify challenges hindering commercialization and propose solutions.
  • To outline a path for translating AND platforms into clinical practice.

Main Methods:

  • Integration of sensitive nanomaterials with machine intelligence for breath biomarker detection.
  • Application of AND platforms across diverse diseases including cancer, asthma, diabetes, and renal failure.
  • Development of wearable systems, smart masks, and multimodal laboratory systems.

Main Results:

  • Demonstrated high diagnostic accuracy (90-95%) for conditions like lung cancer.
  • Achieved sub-parts per billion (ppb) detection limits for biomarkers.
  • Expanded applications into predictive analytics, personalized medicine, and human-machine interaction.

Conclusions:

  • AND platforms represent a transformative approach to healthcare diagnostics.
  • Addressing challenges in data standardization, sensor selectivity, ethical AI, and clinical validation is crucial.
  • Solutions like Explainable AI and large-scale clinical breath databases are needed for clinical translation.