Artificial intelligence in clinical oncology: Multimodal integration and translational development

Area of Science:

• Oncology
• Artificial Intelligence
• Computational Biology

Background:

• Clinical oncology increasingly integrates diverse data (radiology, pathology, genomics, EHRs).
• Data complexity and fragmentation hinder personalized cancer management.
• Traditional single-modality AI systems have limitations in handling multimodal data.

Purpose of the Study:

• To review recent advances in multimodal artificial intelligence (AI) for oncology.
• To highlight AI's role in enhancing personalized cancer care through data integration.
• To discuss challenges and future directions for AI in precision oncology.

Main Methods:

• Review of recent literature on multimodal AI in oncology.
• Synthesis of AI advancements including foundation models, LLMs, and agents.
• Analysis of AI applications in molecular subtyping, risk stratification, and therapeutic recommendations.

Main Results:

• AI enables robust representation learning and cross-modal reasoning for complex cancer data.
• AI platforms accelerate molecular subtyping, risk stratification, and personalized therapy.
• Emerging AI frameworks simulate cellular responses and drug-tumor interactions for predictive insights.

Conclusions:

• Multimodal AI is transforming oncology, enabling more accurate diagnosis, optimized therapy, and accelerated drug development.
• Addressing challenges like generalizability, validation, and ethics is crucial for trustworthy AI deployment.
• AI is shifting oncology towards clinically actionable precision cancer care.