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From Lab to Clinic: Artificial Intelligence with Spectroscopic Liquid Biopsies.

Rose G McHardy¹, James M Cameron¹, David Andrew Eustace¹

¹Dxcover Ltd., Royal College Building, 204 George Street, Glasgow G1 1RX, UK.

Diagnostics (Basel, Switzerland)

|October 29, 2025

Summary

This summary is machine-generated.

Machine learning aids cancer detection using spectroscopic liquid biopsies. Addressing AI explainability and validation is key for clinical use, improving patient outcomes.

Keywords:

artificial intelligence cancer cancer diagnosis liquid biopsy machine learning vibrational spectroscopy

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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.