AI – one size fits all?

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.