Abstract B024: Artificial intelligence enables the ethical reconstruction and social value realization of global cancer research: From technological innovation to humanistic care

Abstract

Abstract The technical breakthrough of artificial intelligence (AI) in the field of oncology has moved from the laboratory to the clinic, but the realization of its social value is still facing the "last mile" dilemma. According to the WHO, there are more than 19 million new cancer cases worldwide every year, but the algorithmic advantages of AI are in sharp contrast to the uneven distribution of resources: while high-income countries are using AI to optimize personalized treatment programs, low-income regions are difficult to enjoy the technical dividends due to the lack of data. This work takes the " Technology-Ethics-Fairness" framework as the starting point to explore how to build a more inclusive AI oncology research ecology through interdisciplinary cooperation. Despite the outstanding performance of AI in the fields of tumor image recognition and genomics analysis, most studies focus on technical performance optimization and ignore the impact of social and cultural differences on the implementation of algorithms. For example, the driver gene mutation characteristics of lung cancer in Asian populations are significantly different from those in Europe and the United States, but the proportion of non-European ancestry samples in the public database is less than 10%, which leads to bias when the model is applied across regions. Furthermore, the inherent "black box" nature of AI decision-making exacerbates the crisis of trust between doctors and patients, especially in areas with limited medical resources, where technical authority may override clinical experience. To foster responsible and equitable AI in oncology, we propose three key pillars so that AI research can better serve society: (1) Data Equity: Establishing a global federated learning consortium for privacy-preserving, multi-omic data sharing to enable cross-regional model training. (2) Interpretability & Trust: Developing "decision traceability" tools that dynamically link AI outputs to clinical guidelines and supporting evidence. (3) Proactive Ethics: Integrating ethical impact assessments, informed by frameworks like the EU AI Act, into clinical trial design, including explicit metrics for equity and bias. The ultimate value of AI should not stop at improving the efficiency of diagnosis and treatment but also reshape the global collaboration network of cancer research. It is recommended to establish an international certification standard of "AI for Oncology," covering the dimensions of data representativeness, algorithm transparency, and cross-cultural adaptability. At the same time, bridging the technology gap through immersive medical education can help doctors in underdeveloped countries or regions to practice AI-assisted decision-making on 3D tumor models. As AI evolves from "technology enabler" to "ecological builder," cancer research will break through the boundaries of regions and disciplines and realize exponential growth of social value. We look forward to seeing more solutions that integrate technological innovation and humanistic care in the future. Citation Format: Zhicheng Du, Lijin Lian, Wenji Xi, Yu Zheng, Gang Yu, Hui-Yan Luo, Peiwu Qin. Artificial intelligence enables the ethical reconstruction and social value realization of global cancer research: From technological innovation to humanistic care [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B024.

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