Artificial Intelligence-Based Treatment Decisions: A New Era for NSCLC

Area of Science:

• Oncology
• Medical Informatics
• Radiology

Background:

• Non-small cell lung cancer (NSCLC) remains a leading cause of cancer mortality globally.
• Current NSCLC treatments, including targeted therapies and immunotherapy, have improved outcomes but require better predictive biomarkers.
• Artificial intelligence (AI) offers advanced computational tools for analyzing large datasets in complex medical problems.

Purpose of the Study:

• To review the current applications of AI in managing NSCLC.
• To focus on AI-driven radiomics and pathomics for NSCLC.
• To discuss the limitations and future directions of AI in NSCLC treatment.

Main Methods:

• Review of current literature on AI applications in NSCLC.
• Focus on radiomics and pathomics as key AI-driven modalities.
• Critical discussion of AI implementation challenges and future potential.

Main Results:

• AI demonstrates significant potential in improving NSCLC diagnosis, treatment guidance, and prognosis.
• Radiomics and pathomics are emerging AI applications with promise for NSCLC management.
• Despite challenges, AI is poised to transform personalized NSCLC treatment strategies.

Conclusions:

• AI represents a paradigm shift in oncology with vast potential for NSCLC.
• Further development and standardization of AI tools are needed for clinical integration.
• The transformative impact of AI on personalized NSCLC treatment is undeniable, warranting continued research and implementation efforts.