# Digital Biomarkers for Precision Early Detection of Lung Cancer: Integrating AI‐Driven Multi‐Omics Into Clinical Pathways

## TL;DR

This review explores how combining AI and multi-omics data can improve early detection of lung cancer and support personalized treatment strategies.

## Contribution

The paper systematically evaluates AI-driven multi-omics biomarkers for early lung cancer detection and their integration into clinical pathways.

## Key findings

- Multi-omics strategies are accelerating the discovery of molecular signatures for early lung cancer detection.
- AI methods help extract patterns from complex data, improving risk stratification and treatment planning.
- Barriers to clinical translation include data heterogeneity, model interpretability, and regulatory challenges.

## Abstract

Lung cancer remains the leading cause of cancer‐related mortality worldwide, highlighting the urgent need for earlier detection within real‐world screening and patient management pathways. Recent advances in multi‐omics technologies have created new opportunities for identifying biomarkers associated with early‐stage lung cancer, particularly in high‐risk populations under clinical surveillance.

This review systematically evaluates early diagnostic biomarkers across multiple omics layers, including genomics, epigenomics, transcriptomics, proteomics, metabolomics and microbiomics. It also summarises the application of artificial intelligence (AI), particularly machine learning and deep learning approaches, for integrating and analysing complex multi‐omics datasets to support biomarker discovery and clinical decision‐making.

Multi‐omics strategies are accelerating the identification of molecular signatures relevant to early lung cancer detection. AI‐driven methods enable the extraction of latent patterns from high‐dimensional data, facilitating risk stratification, diagnostic refinement, histological subtyping and treatment planning. The review highlights the clinical utility of these biomarkers and their potential incorporation into screening algorithms, as well as the development of AI‐based clinical decision support systems (CDSS) aligned with real‐world clinical workflows. However, major barriers to clinical translation remain, including multi‐centre data heterogeneity, limited model interpretability affecting clinical trust, regulatory and cost‐effectiveness challenges and insufficient validation in prospective cohorts.

Emerging technologies, such as single‐cell and spatial multi‐omics, along with federated learning frameworks, offer promising solutions to bridge the gap between computational discovery and clinical implementation. The integration of AI and multi‐omics approaches has the potential to advance risk‐adapted and personalised early detection strategies for lung cancer.