Inteligencia artificial y la era de las soluciones médicas inexplicables: navegando la opacidad algorítmica en la medicina actual.
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| Title: | Inteligencia artificial y la era de las soluciones médicas inexplicables: navegando la opacidad algorítmica en la medicina actual. |
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| Alternate Title: | Artificial intelligence and the age of unexplainable medi cal solutions: navigating algorithmic opacity in today's medicine. |
| Authors: | Regazzoni, Carlos J.1,2 (AUTHOR) cregazzoni@gmail.com |
| Source: | Medicina (Buenos Aires). sep/oct2025, Vol. 85 Issue 5, p1076-1092. 17p. |
| Subjects: | ARTIFICIAL intelligence, MACHINE learning, PREDICTION models, MEDICAL care, CAUSATION (Philosophy), MEDICAL sciences, MEDICAL practice, COMPUTATIONAL complexity |
| Abstract (English): | Artificial intelligence (AI)-based technologies are pro foundly transforming healthcare through machine learn ing 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 ambi guity, thus optimizing diagnostics, treatment, healthcare management, and biomedical research. However, this technological revolution presents an unprecedented epistemological challenge whereas traditional statis tics 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, con trasts 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 inacces sible even to their developers. This divergence poses significant educational, professional, and ethical chal lenges, 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, con trasting both approaches from the inferential process to causal evaluation. It emphasizes the urgent necessity for medicine to deeply comprehend these emerging para digms in order to critically integrate them into clinical practice and scientific research. [ABSTRACT FROM AUTHOR] |
| Abstract (Spanish): | Las tecnologías basadas en inteligencia artificial (IA) están transformando profundamente el cuidado de la salud mediante técnicas de machine learning (o aprendi zaje automático) y redes neuronales. Estas herramientas estadísticas revolucionan la programación determinista clásica al aprender directamente patrones probabilísticos a partir de los datos. Estos sistemas permiten abordar problemas clínicos antes inaccesibles por complejidad o ambigüedad, optimizando así diagnóstico, tratamiento, gestión sanitaria e investigación biomédica. Sin embargo, esta revolución tecnológica plantea un desafío epistemo lógico sin precedentes mientras la estadística tradicional busca explicaciones causales de los fenómenos bajo observación, los modelos predictivos de IA priorizan el rendimiento predictivo, independientemente de la comprensión teórica subyacente. Esta "opacidad algorít mica", derivada de modelos con millones de parámetros ajustados automáticamente, contrasta con el razona miento clínico humano basado en métodos analíticos o heurísticos. La medicina, que históricamente fundamenta sus decisiones en causalidad y evidencia explicativa, se enfrenta ahora a herramientas predictivas cuya lógica interna frecuentemente resulta inaccesible incluso para sus desarrolladores. Esta divergencia genera retos signifi cativos a nivel educativo, profesional y ético, requiriendo que los médicos adquieran nuevos equipamientos con ceptuales en estadística avanzada e informática para na vegar efectivamente esta transición. Este artículo explora las bases bioestadísticas y conceptuales de esta tensión entre predicción y explicación en IA, contrastando ambas aproximaciones desde el proceso inferencial hasta la evaluación causal, destacando la necesidad imperiosa de que la medicina comprenda profundamente estos nuevos paradigmas para integrarlos críticamente en la práctica clínica y la investigación científica. [ABSTRACT FROM AUTHOR] |
| Copyright of Medicina (Buenos Aires) is the property of Medicina (Buenos Aires) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | MedicLatina |
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| Abstract: | Artificial intelligence (AI)-based technologies are pro foundly transforming healthcare through machine learn ing 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 ambi guity, thus optimizing diagnostics, treatment, healthcare management, and biomedical research. However, this technological revolution presents an unprecedented epistemological challenge whereas traditional statis tics 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, con trasts 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 inacces sible even to their developers. This divergence poses significant educational, professional, and ethical chal lenges, 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, con trasting both approaches from the inferential process to causal evaluation. It emphasizes the urgent necessity for medicine to deeply comprehend these emerging para digms in order to critically integrate them into clinical practice and scientific research. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00257680 |
