Artificial intelligence-assisted point-of-care devices for lung cancer

Area of Science:

• Biomedical Engineering
• Oncology
• Artificial Intelligence

Background:

• Lung cancer is a leading cause of cancer death globally, often detected late, limiting treatment efficacy.
• Traditional imaging methods for lung cancer screening are expensive and inconvenient.
• Point-of-care biosensors offer a sensitive, selective, and minimally invasive alternative for early detection.

Purpose of the Study:

• To review recent advances in biosensors for lung cancer screening and detection.
• To explore the role of artificial intelligence (AI) in enhancing biosensor performance.
• To discuss challenges in clinical translation and future perspectives of AI-coupled biosensors.

Main Methods:

• Review of current literature on biosensors for lung cancer detection.
• Analysis of AI integration in biosensor technology for improved sensitivity and selectivity.
• Examination of biomarker selection, standardization, and clinical cut-off value determination.

Main Results:

• Biosensors can detect lung cancer biomarkers in various biological fluids with high sensitivity.
• AI integration significantly improves data analysis, patient differentiation, and metastasis risk prediction.
• Challenges remain in standardizing biomarker panels and clinical validation for widespread adoption.

Conclusions:

• AI-coupled biosensors show significant promise for early lung cancer detection and risk assessment.
• Addressing standardization and clinical validation is crucial for translating these technologies into practical healthcare solutions.
• Future AI-assisted biosensors could evolve into comprehensive health monitoring systems.