Artificial intelligence derived virtual oncology

Chunyi Lyu; Dongbo Yao; Fangjing Yin; Zhaonian Liu; Bingqiang Liu; Huixian Ma; Tao Sun

doi:10.59717/j.xinn-oncol.2026.100028

The Innovation Oncology > 2026 Vol. 1 > No. 2 > 100028

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Artificial intelligence derived virtual oncology

1.
Department of Hematology, Qilu Hospital of Shandong University, Jinan 250012, China
2.
Medical Integration and Practice Center, Shandong University, Jinan 250012, China
3.
School of Mathematics, Shandong University, Jinan 250100, China
4.
State Key Laboratory of Cryptography and Digital Economy Security, Shandong University, Jinan 250100, China
5.
Stem Cell and Regenerative Medicine Research Center, Qilu Hospital, Shandong University, Jinan 250012, China
6.
Shandong Key Laboratory of Hematological Diseases and Immune Microenvironment, Jinan 250012, China
7.
Shandong Provincial Clinical Research Center for Hematological Diseases, Jinan 250012, China
8.
These authors contributed equally to this work

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Corresponding authors: taosun@sdu.edu.cn (T.S.); mahuixian9616@email.sdu.edu.cn (H.M.); bingqiang@sdu.edu.cn (B.L.)
Received Date: April 25, 2026

Accepted Date: July 16, 2026

Published Online: July 23, 2026

The Innovation Oncology 1(2), https://doi.org/10.59717/j.xinn-oncol.2026.100028 | Cite this article

GRAPHICAL ABSTRACT

GRAPHICAL ABSTRACT

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PUBLIC SUMMARY

PUBLIC SUMMARY
1. Artificial intelligence virtual oncology (AIVO) extends individual-cell modeling to tumor ecosystems.
  
  AIVO as a virtual entity uses dynamic simulation to improve diagnosis and treatment for precision oncology.
  
  AIVO remains conceptual, facing challenges in standards, reproducibility, validation, regulation, and equity.
  
  Further interdisciplinary research and real-world validation are needed before AIVO becomes a reliable tool.

ABSTRACT

ABSTRACT

Tumors are dynamic multicellular ecosystems in which heterogeneity, evolution, and cross-scale interactions remain major barriers to precision oncology. Although multiple technological advances have improved tumor characterization, they remain largely static and descriptive, limiting their ability to capture state transitions, treatment-induced remodeling, and evolving intercellular communication. Artificial intelligence virtual cell (AIVC) provides a foundation for modeling cellular states, perturbation responses, and functional outputs from multimodal data. We extend this concept to artificial intelligence virtual oncology (AIVO), a framework designed to reconstruct multicellular communication, coordinated evolution, and system-level remodeling among tumor, immune, and stromal compartments. This review summarizes the conceptual basis, construction strategies, and emerging applications of this progression from AIVC to AIVO in tumor diagnosis, dynamic monitoring, treatment response prediction, and drug screening. We further discuss key challenges in data integration, interpretability, patient-specific calibration, and clinical validation. Grounded in AIVC-based modeling, we propose AIVO as an emerging framework for multimodal integration, dynamic modeling, and decision support in future oncology.
- Oncology/ /
- Multicellular /
- communication/ /
- Artificial /
- intelligence/ /
- Artificial /
- intelligence /
- virtual /
- cell/ /
- Artificial /
- intelligence /
- virtual /
- oncology

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Lyu C., Yao D., Yin F., et al. (2026). Artificial intelligence derived virtual oncology. The Innovation Oncology 1:100028. https://doi.org/10.59717/j.xinn-oncol.2026.100028

Lyu C., Yao D., Yin F., et al. (2026). Artificial intelligence derived virtual oncology. The Innovation Oncology 1:100028. https://doi.org/10.59717/j.xinn-oncol.2026.100028

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