Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy.

This publication is a narrative review of how artificial intelligence and machine learning are being applied to advance cancer immunotherapy, with emphasis on current capabilities, limitations, and translational potential.

Key points:

1. Scope of AI in cancer immunotherapy

• Focuses on four major application areas:
• • Patient stratification – using ML models to predict which patients are likely to respond or develop resistance to immunotherapies (e.g., checkpoint inhibitors), typically by integrating clinical, imaging, and multi-omics data.
• • Biomarker discovery – leveraging high-dimensional data (genomics, transcriptomics, proteomics, single-cell, spatial data) to find predictive and prognostic biomarkers of response, toxicity, and resistance.
• • Treatment strategy optimization – applying AI to guide regimen selection, dosing, treatment sequencing, and combination strategies (e.g., immunotherapy plus chemotherapy, targeted therapy, or radiation).
• • Foundation models and large language models – reviewing emerging work using large, pre-trained models to integrate diverse biomedical datasets and knowledge sources for hypothesis generation, decision support, and more robust prediction tools in immuno-oncology.

2. Role of multi-omics and data integration

• • Emphasizes that the greatest gains come from combining heterogeneous data types rather than relying on single biomarkers.
• • Describes how deep learning architectures are being used to integrate multi-omics, pathology images, and clinical data to better capture tumor–immune interactions and microenvironmental features relevant to immunotherapy response.

3. Clinical translation and limitations

• • Notes that, despite promising performance in research settings, many AI models face barriers to routine clinical use:
• • Limited generalizability across cohorts, institutions, and sequencing platforms.
• • Small, biased, or non-representative training datasets.
• • Lack of interpretability and biological plausibility.
• • Regulatory, ethical, and data-sharing challenges.
• • Highlights the gap between methodological innovation and validated clinical decision-support tools actually used in practice.

4. Future directions and recommendations

• • Calls for:
• • Larger, more diverse, and better-annotated datasets, ideally from multi-center collaborations.
• • Prospective validation and clinical trials embedding AI-based tools for patient selection and treatment guidance.
• • Methods that improve interpretability and link model outputs to testable biological mechanisms.
• • Continued development of foundation models and LLM-based systems tailored to immuno-oncology, with rigorous evaluation of their real-world utility.

For a practicing oncologist, the take-home message is that AI/ML is rapidly reshaping how we identify immunotherapy candidates, discover biomarkers, and design treatment strategies, but most tools remain early-stage. The review serves mainly to map the field, highlight promising directions, and outline what is needed to move from research models to clinically reliable decision support in precision immunotherapy.