From Lab to Clinic: Artificial Intelligence with Spectroscopic Liquid Biopsies

Area of Science:

• Oncology
• Biomedical Engineering
• Artificial Intelligence

Background:

• Machine learning (ML) and artificial intelligence (AI) are integral to advanced cancer detection, especially in multi-omic analyses like spectroscopic liquid biopsies.
• The high dimensionality of spectral data necessitates ML for identifying subtle cancer signatures.
• Transitioning AI-driven medical devices from research to clinical practice faces significant regulatory hurdles.

Purpose of the Study:

• To review the challenges in clinically implementing AI-powered spectroscopic liquid biopsies.
• To highlight critical factors for regulatory approval and patient adoption.
• To emphasize the need for explainable AI and robust validation in this field.

Main Methods:

• Review of current literature on AI in spectroscopic liquid biopsies.
• Analysis of regulatory considerations for AI medical devices.
• Discussion of technical requirements for clinical translation.

Main Results:

• Spectroscopic liquid biopsies show promise for cancer detection via ML pattern recognition.
• Explainable AI and diverse, representative validation datasets are crucial for clinical trust and regulatory approval.
• Overcoming these challenges is vital for accelerating the adoption of these technologies.

Conclusions:

• AI, particularly ML, is essential for interpreting complex spectroscopic liquid biopsy data for cancer detection.
• Successful clinical integration requires addressing explainability and validation rigor.
• Accelerating clinical uptake of these advanced diagnostics can significantly improve patient survival and quality of life.