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<p>Artificial intelligence (AI) is increasingly evolving from a&#xa0;research technology into a&#xa0;tool for everyday clinical practice. While early applications primarily focused on medical image analysis, generative AI systems and large language models are now available for a&#xa0;wide range of clinical and administrative tasks. These include medical documentation, literature review, guideline-based knowledge management, patient communication, and workflow optimization. At the same time, diagnostic and therapeutic applications continue to evolve. AI-assisted systems support radiological and pathological image interpretation, risk stratification, and clinical decision-making processes. Despite considerable opportunities, important limitations remain. AI hallucinations, algorithmic bias, data protection requirements, and regulatory considerations necessitate continuous human oversight and critical evaluation. Therefore, the long-term success of AI will depend not only on technological performance but also on its responsible integration into existing healthcare structures. This review provides a&#xa0;practice-oriented overview of current and future AI applications in urology and discusses opportunities, limitations, and prerequisites for safe implementation in clinical practice and hospital care.</p>

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Nutzung von künstlicher Intelligenz in Klinik und Praxis

  • 期刊论文

  • 发表日期：

    2026年7月30日

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  • Hendrik Borgmann

摘要

Artificial intelligence (AI) is increasingly evolving from a research technology into a tool for everyday clinical practice. While early applications primarily focused on medical image analysis, generative AI systems and large language models are now available for a wide range of clinical and administrative tasks. These include medical documentation, literature review, guideline-based knowledge management, patient communication, and workflow optimization. At the same time, diagnostic and therapeutic applications continue to evolve. AI-assisted systems support radiological and pathological image interpretation, risk stratification, and clinical decision-making processes. Despite considerable opportunities, important limitations remain. AI hallucinations, algorithmic bias, data protection requirements, and regulatory considerations necessitate continuous human oversight and critical evaluation. Therefore, the long-term success of AI will depend not only on technological performance but also on its responsible integration into existing healthcare structures. This review provides a practice-oriented overview of current and future AI applications in urology and discusses opportunities, limitations, and prerequisites for safe implementation in clinical practice and hospital care.