KI-Anwendungen in der nephrologischen Diagnostik

Abstract

Artificial intelligence (AI) is rapidly reshaping medical diagnostics, and nephrology - characterized by multifactorial disease patterns - stands to benefit markedly. Machine‑learning and deep‑learning algorithms, especially convolutional neural networks (CNNs) and large language models (LLMs), can analyze heterogeneous data streams from electronic health records, imaging, histopathology and genomics to support diagnosis, prognosis and therapeutic planning. AI‑driven automation of routine workflows (e.g., appointment scheduling, NLP‑based documentation, chatbot‑guided anamneses) enables clinicians to focus on complex decision‑making, while real‑time decision‑support tools can integrate laboratory, imaging and guideline data. Recent advances include CNN‑based detection of renal lesions, deep‑learning prognostic scores for IgA nephropathy, and AI‑enhanced variant calling (e.g., DeepVariant). Nevertheless, challenges persist: data bias, limited external validation, "hallucinations" of LLMs, regulatory compliance (MDR, GDPR), and the need for transparent, locally hosted models. Successful implementation requires interoperable, FHIR‑compatible data, robust training of staff, and integration of AI education into medical education. With this, AI promises substantial efficiency gains, improved diagnostic precision, and sustained care quality in nephrology.

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