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<p>Artificial intelligence (AI) offers new opportunities for personalized medicine in anesthesia and intensive care medicine. Realizing this potential requires a&#xa0;representative, high-quality, and “as-bias-free-as-possible” data foundation. This article examines the “data journey” of clinical AI models and demonstrates how systematic biases can subtly infiltrate algorithms behind the veil of technical objectivity. Rather than viewing AI as a&#xa0;“black box”, clinicians are encouraged to understand model limitations and critically evaluate results to ensure the safe and equitable use of AI for all patient groups.</p>

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Was Anästhesist:innen über faire KI wissen sollten

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月3日

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  • Lorenz Kapral,
  • Helena Schluchter,
  • Oliver Kimberger

摘要

Artificial intelligence (AI) offers new opportunities for personalized medicine in anesthesia and intensive care medicine. Realizing this potential requires a representative, high-quality, and “as-bias-free-as-possible” data foundation. This article examines the “data journey” of clinical AI models and demonstrates how systematic biases can subtly infiltrate algorithms behind the veil of technical objectivity. Rather than viewing AI as a “black box”, clinicians are encouraged to understand model limitations and critically evaluate results to ensure the safe and equitable use of AI for all patient groups.