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REVIEW ARTICLE

From cancer treatment strategies to survivorship care: Advances in integrating multi-omics and artificial intelligence

Ling Yin1,2 ,  Bao Chun Cheng3 ,  Xueyi Wang3 ,  Li Xia3 ,  Lei Ye3 ,  Xuefei Ji3 ,  Hui Wang3 ,  Lu Gao3*
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1 Department of Medicine, Weill Cornell Medicine, Cornell University, New York, United States of America
2 Department of Epidemiology and Biostatistics, College of Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
3 Department of Neurosurgery, First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
Global Translational Medicine, 025470087 https://doi.org/10.36922/GTM025470087
Received: 17 November 2025 | Revised: 12 December 2025 | Accepted: 21 January 2026 | Published online: 9 February 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Advances in oncology are successfully shifting clinical focus beyond initial tumor control to address the complex, lifelong needs of a growing survivor population. This review examines the evolution of therapeutic paradigms, dissecting the mechanistic underpinnings and clinical impact of modern immunotherapies and gene-based modalities. It further addresses the multifaceted challenges in survivorship care, from managing persistent morbidity and psychological distress to overcoming systemic barriers in care coordination. Central to this discussion is the promise of multi-omics—which offers a high-resolution lens into tumor and host biology—to enable preemptive risk profiling. Concurrently, artificial intelligence (AI) emerges as the computational engine to distill these complex datasets into actionable intelligence for personalized surveillance. Furthermore, this review explores the translational hurdles facing AI, with a critical examination of inherent vulnerabilities, such as algorithmic bias, model opacity, and ethical pitfalls, in clinical deployment. By synthesizing these domains, this article provides a conceptual framework to steer the evolution of evidence-based, equitable, and patient-focused survivorship care.

Keywords
Cancer treatment strategy
Cancer survivorship care
Multi-omics
Artificial intelligence
Personalized medicine
Funding
None.
Conflict of interest
Ling Yin is the Guest Editor of this special issue, but was not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. Separately, other authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.
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