ReviewAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026
Full-Body AI Agent: A Perspective on Multi-Scale Collaborative AI for Systemic Biology and Precision Medicine.
Review in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Artificial intelligence (AI) is increasingly applied to biomedical research, but most current systems remain limited to specific tasks, data types, or biological scales. This makes it difficult to connect molecular alterations, organelle dysfunction, cellular behavior, tissue remodeling, organ physiology, systemic regulation, and whole-body phenotypes into coherent biological reasoning. In this Perspective, we propose the Full-Body AI Agent as a hypothetical multi-agent framework and conceptual blueprint for future systemic biology and precision medicine, rather than a fully implemented software platform. This framework envisions a supervisory Full-Body AI Agent coordinating seven biological-level agents, namely Molecule, Organelle, Cell, Tissue, Organ, Organ System, and Body System AI Agents, to standardize biomedical data, decompose cross-scale questions, assign level-specific tasks, and integrate outputs through iterative feedback. We further outline the data commons, harmonization mechanisms, uncertainty handling, arbitration strategies, and traceability safeguards required for biologically grounded cross-scale reasoning. Two hypothetical scenarios, metastasis analysis and drug development, illustrate how this framework could organize multilevel evidence from molecular changes to systemic phenotypes and therapeutic responses. This Perspective aims to clarify the conceptual basis of full-body AI and provide a foundation for transparent, physiology-constrained, cross-scale AI systems in disease analysis, therapeutic evaluation, and personalized medicine.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.