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Updated: Jul 5, 2026

Microfluidic Co-Culture Models for Dissecting the Immune Response in in vitro Tumor Microenvironments
Published on: April 30, 2021
Emerging artificial intelligence advances in oncology: latest updates from the 2026 AACR annual meeting
Yan-Ruide Li1,2,3, Zhengyao Shao4, Haochen Nan5
1Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, CA, USA. charlie.li@ucla.edu.
Abstract:
Advances presented at the 2026 American Association for Cancer Research (AACR) Annual Meeting highlight a shift from standalone artificial intelligence (AI) models to integrated, agentic systems across oncology. Platforms such as Synapse enable large-scale data coordination, supporting interoperable and reproducible research. Building on this foundation, conversational and multi-agent AI tools (e.g., DrBioRight, GP CoPilot, Isabl AI Agent) allow natural language interaction with multimodal cancer data, lowering technical barriers. Agentic frameworks for real-world data (RWD) transformation, including clinical document abstraction, cohort extraction, and social determinants of health (SDOH) analysis, demonstrate high accuracy and scalability, while self-critical systems improve reliability. Clinically, AI shows growing impact through validated imaging biomarkers, enhanced trial matching, and scalable cohort identification. In parallel, multi-agent systems are accelerating therapeutic discovery, including CAR-T development and immunotherapy target identification. Collectively, these advances position AI as an active, collaborative partner in cancer research and precision oncology.
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