Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy.

Area of Science:

• Oncology
• Immunology
• Artificial Intelligence

Background:

• Cancer immunotherapy has advanced oncology treatment, but challenges persist in predicting responses and resistance.
• Artificial intelligence (AI) and machine learning (ML) offer powerful tools to overcome these hurdles.
• Existing deep learning frameworks, including large language models (LLMs), are increasingly used with multi-omics data in cancer research.

Purpose of the Study:

• To systematically review and critically evaluate the applications and translational potential of AI/ML in cancer immunotherapy.
• To examine current developments and future directions in AI for cancer immunotherapy.
• To identify actionable insights for integrating AI/ML into precision cancer immunotherapy.

Main Methods:

• Comprehensive review of AI/ML applications in cancer immunotherapy.
• Discussion of four key areas: patient stratification, biomarker discovery, treatment strategy optimization, and foundation models/LLMs.
• Critical evaluation of current limitations and future directions of AI approaches.

Main Results:

• AI/ML applications are enabling breakthroughs in patient stratification and biomarker discovery.
• Foundation models and LLMs are being developed to integrate complex multi-omics data for cancer immunotherapy.
• The review identifies key areas where AI/ML can accelerate progress in precision oncology.

Conclusions:

• AI/ML holds significant promise for advancing cancer immunotherapy by addressing critical challenges.
• Systematic evaluation and strategic integration of AI/ML are crucial for realizing its full translational potential.
• Future research should focus on overcoming limitations and developing roadmaps for AI-driven precision cancer immunotherapy.