Artificial Intelligence and Multi-Agent Systems in Oncology:Clinical Translation, Infrastructure, and Future Directions
DOI:
https://doi.org/10.66765/acobs.2026.003Keywords:
Artificial intelligence, Multi-agent systems, Oncology, Radiomics, Pharmacogenomics, Federated learning, Nuclear medicine, Precision oncology, Multi-omics integration, Clinical translationAbstract
Background: Artificial intelligence (AI) and multi-omics integration have rapidly advanced oncology by enabling analysis of genomics, proteomics, and other biological data. Deep learning techniques now support automated tumour segmentation and drug response prediction using large cancer databases.
Objective: To review key developments, clinical applications, and challenges of AI-driven precision oncology.
Methods: A narrative overview of current literature on AI in imaging, radiomics, pharmacogenomics, federated systems, and interoperability standards.
Results: Radiomics enables quantitative tumour characterization from imaging, while machine learning improves anticancer drug sensitivity modelling. Emerging technologies include autonomous AI agents and federated multi-site systems that enable decentralized, privacy-preserving clinical implementation.
Conclusion: AI-driven multi-omics and multi-agent systems are transforming precision oncology, though challenges in infrastructure, regulation, and clinical translation remain.
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