[Artificial intelligence and the age of unexplainable medical solutions: navigating algorithmic opacity in today's...

Artificial intelligence (AI)-based technologies are profoundly transforming healthcare through machine learning techniques and neural networks. These statistical tools revolutionize classical deterministic programming by directly learning probabilistic patterns from data. Such systems enable the resolution of clinical problems previously inaccessible due to their complexity or ambiguity, thus optimizing diagnostics, treatment, healthcare management, and biomedical research. However, this technological revolution presents an unprecedented epistemological challenge: whereas traditional statistics seek causal explanations for observed phenomena, predictive AI models prioritize predictive performance regardless of the underlying theoretical understanding. This "algorithmic opacity," resulting from models with millions of autonomously adjusted parameters, contrasts with human clinical reasoning based on analytical or heuristic methods. Medicine, historically grounded in causality and explanatory evidence, now confronts predictive tools whose internal logic is often inaccessible even to their developers. This divergence poses significant educational, professional, and ethical challenges, requiring physicians to acquire new conceptual competencies in advanced statistics and informatics to effectively navigate this transition. This article examines the biostatistical and conceptual foundations of this tension between prediction and explanation in AI, contrasting both approaches from the inferential process to causal evaluation. It emphasizes the urgent necessity for medicine to deeply comprehend these emerging paradigms in order to critically integrate them into clinical practice and scientific research.