AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data

In recent years, the advent of deep learning and, in particular, transformer-based architectures, has significantly revolutionized the field of Artificial Intelligence (AI) in many scientific domains, including computer vision, natural language processing, and sequence modeling, thanks to the increasing availability of computational power and large-scale data-sets. However, classical Machine Learning (ML) methods, such as decision trees, gradient-boosted trees, Support V ector Machines (SVMs), and regression--based techniques, continue to be considered as the state-of-the-art for tabular data, which are still nowadays widely used in healthcare, finance, industrial monitoring, and other structured-data domains. There are several reasons for this. Notably, conventional AI models tend to perform reasonably well on datasets of limited size, whereas state-of-the-art deep learning techniques typically require substantially larger amounts of data to generalize effectively. Moreover, many classical AI methods, such as regression, Bayesian approaches, rule-based systems, and tree-based models, are inherently more interpretable, a characteristic that is particularly valuable in high-stakes domains such as healthcare. In contrast, deep learning models often work as black boxes, limiting their explainability. As an example, Grinsztajn et al. [1] showed that tree-based ensembles like XGBoost and Random Forests consistently outperformed a wide range of contemporary deep learning models across dozens of medium-sized tabular datasets (