Traidl Stephan, Mathes Sonja, Seurig Sebastian
Department of Dermatology and Allergy, Hannover Medical School, Hanover.
Department of Dermatology and Allergy, School of Medicine and Health, Technical University of Munich, Munich, and.
Allergol Select. 2025 Aug 8;9:75-79. doi: 10.5414/ALX02568E. eCollection 2025.
The use of artificial intelligence (AI) in medicine requires a careful selection of suitable models, as there is no universal "one size fits all" method. While linear regression is convincing due to its simplicity and interpretability, it is limited due to the assumption of linearity and susceptibility to multicollinearity and outliers. More complex approaches such as neural networks show their strengths in the detection of non-linear patterns and automatic feature extraction but require large amounts of data, high computing capacity, and suffer from limited explainability. Principal component analysis (PCA) offers an efficient reduction of dimensionality. Ultimately, the choice of model depends on the balance between accuracy, interpretability, and data availability. A selection of machine learning models is presented in this article.
在医学中使用人工智能（AI）需要谨慎选择合适的模型，因为不存在通用的“一刀切”方法。虽然线性回归因其简单性和可解释性而令人信服，但由于其线性假设以及对多重共线性和异常值的敏感性，它存在局限性。诸如神经网络等更复杂的方法在检测非线性模式和自动特征提取方面显示出优势，但需要大量数据、高计算能力，并且可解释性有限。主成分分析（PCA）提供了一种有效的降维方法。最终，模型的选择取决于准确性、可解释性和数据可用性之间的平衡。本文介绍了一系列机器学习模型。
