Artificial Intelligence-Powered Nanosensor Platforms for Non-Invasive Breathomic Diagnostics.

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.