Review: Artificial Intelligence Applied to Physiological Signals to Support Clinical Diagnosis

Analyzing physiological signals with artificial intelligence (AI) has become a strategic tool for clinical diagnosis, event prediction, risk stratification, monitoring, and functional analysis, among other tasks. However, the growing use of AI in healthcare raises a key concern: models can achieve high performance on specific tasks, but this does not necessarily translate into clinical utility, generalizability, or integration into real-world care settings. This work presents a structured review of recent and selected foundational research published between 2018 and 2026, aimed at characterizing AI applications to physiological signals. It considers the analyzed signals, AI families, implemented techniques, reported metrics, the degree of unimodal or multimodal integration, and gaps in validation, interpretability, and reproducibility. The final database included 83 evidence entries, organized as an evidence matrix with information on AI family, architecture, reported performance, and the physiological or functional signal used. This review identifies single-source approaches, multimodal predictive integration, and multimodal representation learning, highlighting their different methodological characteristics and potential clinical applications. The main contribution of this work is to show that the potential clinical contribution of AI applied to physiological signals depends not only on algorithmic performance but also on its integration with robust validation, traceability, interpretability, physiological coherence, and relevance to the clinical phenomenon.