The Current Status and Evaluation of Artificial Intelligence in Bladder Cancer: Clinical Applications and Research.

IF 4.8 2区生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY

Computational and structural biotechnology journal Pub Date : 2026-10-19 eCollection Date: 2026-01-01 DOI:10.34133/csbj.0197

Zhuo Yin, Joshua W K Ho

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引用次数: 0

Abstract

Bladder cancer ranks as the 10th most common malignancy worldwide. Advances in technology are continuously reshaping its diagnosis, treatment selection, and prognosis assessment. In recent years, artificial intelligence (AI) has demonstrated marked potential in transforming bladder cancer care. By integrating and analyzing multi-source data, AI enhances the precision and efficiency of disease management. This review comprehensively examines the current applications of AI across various domains, including radiographic imaging analysis, pathological diagnosis, cystoscopic image processing, molecular subtyping, drug development, organoid research, prognostic modeling, and medical device innovation. It highlights the advantages of AI over conventional methods, outlines model development strategies, and summarizes commonly employed algorithms. Furthermore, we discuss the key challenges in translating AI tools into real-world clinical practice. This narrative review aims to inform the future development of AI-driven digital health systems, ultimately contributing to the realization of precision medicine and personalized management for bladder cancer patients.

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人工智能在膀胱癌治疗中的现状与评价：临床应用与研究。

膀胱癌在全球最常见的恶性肿瘤中排名第十。技术进步不断重塑其诊断、治疗选择和预后评估。近年来，人工智能（AI）在改变膀胱癌治疗方面显示出显著的潜力。人工智能通过对多源数据的整合和分析，提高了疾病管理的精准度和效率。本文综述了目前人工智能在各个领域的应用，包括放射成像分析、病理诊断、膀胱镜图像处理、分子分型、药物开发、类器官研究、预后建模和医疗器械创新。它强调了人工智能相对于传统方法的优势，概述了模型开发策略，并总结了常用的算法。此外，我们还讨论了将人工智能工具转化为现实世界临床实践的关键挑战。本文旨在为人工智能驱动的数字医疗系统的未来发展提供信息，最终实现膀胱癌患者的精准医疗和个性化管理。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computational and structural biotechnology journal Biochemistry, Genetics and Molecular Biology-Biophysics

CiteScore

9.30

自引率

3.30%

发文量

540

审稿时长

6 weeks

期刊介绍： Computational and Structural Biotechnology Journal (CSBJ) is an online gold open access journal publishing research articles and reviews after full peer review. All articles are published, without barriers to access, immediately upon acceptance. The journal places a strong emphasis on functional and mechanistic understanding of how molecular components in a biological process work together through the application of computational methods. Structural data may provide such insights, but they are not a pre-requisite for publication in the journal. Specific areas of interest include, but are not limited to: Structure and function of proteins, nucleic acids and other macromolecules Structure and function of multi-component complexes Protein folding, processing and degradation Enzymology Computational and structural studies of plant systems Microbial Informatics Genomics Proteomics Metabolomics Algorithms and Hypothesis in Bioinformatics Mathematical and Theoretical Biology Computational Chemistry and Drug Discovery Microscopy and Molecular Imaging Nanotechnology Systems and Synthetic Biology