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<p>Artificial intelligence (AI) is advancing clinical oncology, yet its application to abdominal tumors presents distinct challenges compared to other cancer types. This review analyzes the role of machine intelligence specifically in abdominal oncology, focusing on its scope in detection, diagnosis, and treatment optimization. We find that AI systems, particularly computer-aided detection (CAD) and diagnosis (CADx), demonstrate high performance in tumor identification and characterization. Furthermore, radiomics powered by these computational tools can non-invasively predict molecular profiles, aiding personalized therapy. However, key challenges—including data heterogeneity, model generalizability, ethical concerns, and regulatory gaps—hinder robust clinical integration. This review synthesizes current applications, critically examines domain-specific limitations, and proposes a framework for translating AI advancements into improved clinical management of abdominal tumors. Future directions to bridge this translational gap include privacy-preserving federated learning, explainable AI for clinical trust, and rigorous multi-center validation.</p>

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Overcoming domain-specific challenges for artificial intelligence in abdominal oncology toward clinical translation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月22日

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  • Boshi Duan,
  • Xue Bai,
  • Tianzuo Wang,
  • Tianyou Wang,
  • Xiao Hu,
  • Shupei Li

摘要

Artificial intelligence (AI) is advancing clinical oncology, yet its application to abdominal tumors presents distinct challenges compared to other cancer types. This review analyzes the role of machine intelligence specifically in abdominal oncology, focusing on its scope in detection, diagnosis, and treatment optimization. We find that AI systems, particularly computer-aided detection (CAD) and diagnosis (CADx), demonstrate high performance in tumor identification and characterization. Furthermore, radiomics powered by these computational tools can non-invasively predict molecular profiles, aiding personalized therapy. However, key challenges—including data heterogeneity, model generalizability, ethical concerns, and regulatory gaps—hinder robust clinical integration. This review synthesizes current applications, critically examines domain-specific limitations, and proposes a framework for translating AI advancements into improved clinical management of abdominal tumors. Future directions to bridge this translational gap include privacy-preserving federated learning, explainable AI for clinical trust, and rigorous multi-center validation.