This narrative review examines how AI can link imaging, pathology, and multi-omics data to smarter care in Lung Cancer, spanning early screening, prognostic risk assessment, precision treatment, drug sensitivity analysis, and personalized surgical planning. The paper argues that multimodal AI may help clinicians build a more complete disease atlas from molecular changes to imaging phenotypes, but emphasizes that real-world adoption still faces major translation barriers.

Why It Matters To Your Practice

• Lung cancer remains difficult to manage because of tumor heterogeneity, a complex microenvironment, metastatic potential, and drug resistance.

• Clinicians are increasingly confronted with fragmented data from sequencing, imaging, and digital pathology that are hard to synthesize with traditional approaches.

• Multimodal AI offers a framework to integrate these data streams and potentially improve decision-making across the care pathway.

Clinical Implications

• Potential use cases include earlier screening and detection, more refined prognostic stratification, and better matching of patients to precision therapies.

• AI-driven fusion of multi-omics and imaging data may support drug sensitivity analysis and help anticipate resistance patterns.

• For proceduralists and surgeons, these models may eventually inform more personalized surgical planning.

Insights

• The review frames multimodal AI as a way to connect microscopic molecular variation with macroscopic imaging phenotypes.

• This cross-modal approach could move lung cancer assessment beyond siloed tests toward a more panoramic view of disease biology.

• Key barriers include data heterogeneity, workflow integration, standardization, validation, and the broader challenge of translating research models into clinical practice.

The Bottom Line

• Multimodal AI in Lung Cancer is promising because it could link imaging and labs to smarter, more personalized care from screening to surgery.

• For now, the biggest clinician takeaway is not immediate replacement of current workflows, but preparation for tools that will need strong validation, interoperability, and implementation planning before routine use.