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

## TL;DR

This paper discusses how AI can help detect cancer using liquid biopsies, but highlights challenges in moving these tools from research to real-world medical use.

## Contribution

The paper emphasizes the importance of explainable AI and diverse validation in advancing spectroscopic liquid biopsies for clinical use.

## Key findings

- AI is essential for analyzing complex spectral data in cancer detection.
- Regulatory and explainability challenges hinder clinical adoption of AI tools.
- Diverse validation sets are needed to ensure reliable clinical performance.

## Abstract

Over recent years, machine learning and artificial intelligence have become critical components of many cancer detection tests, in particular multi-omic tests such as spectroscopic liquid biopsies. The complexity and multi-variate nature of spectral datasets makes machine learning invaluable in uncovering patterns that enable robust differentiation of cancer signals. However, introducing any AI-enabled medical device into clinical practice is challenging due to the regulatory requirements needed to progress from fundamental research to clinical and patient use. This review explores some of the fundamental concerns in bringing spectroscopic liquid biopsies to the clinic, including the need for explainable artificial intelligence and diverse validation sets. Addressing these issues is essential to accelerate clinical uptake with the ultimate goal of improving patient survival and quality of life.