Resumen

SAHOO, Soudaminee; RANI-PANIGRAHI, Chhabi  y  PATI, Bibudhendu. IoMT-Enabled Smart Healthcare Models to Monitor Critical Patients Using Deep Learning Algorithms: A Review. Comp. y Sist. [online]. 2024, vol.28, n.4, pp.1823-1832.  Epub 25-Mar-2025. ISSN 2007-9737.  https://doi.org/10.13053/cys-28-4-4915.

A new era of healthcare transformation has begun with the combination of deep learning and the Internet of Medical Things (IoMT). In this review, we explore the transformative potential of IoMT-enabled Smart Healthcare (SHC) models for the unceasing monitoring of critical patients by leveraging the power of deep learning algorithms. The IoMT, a network of interconnected medical devices and applications has revolutionized the acquisition and transmission of real-time patient data. Simultaneously, deep learning algorithms have demonstrated exceptional proficiency in deciphering complex patterns within vast healthcare datasets. By synergizing these technologies, SHC models have emerged as a promising solution to the pressing challenges of critical patient care. This review provides an extensive insight into the latest developments and methodologies at the intersection of IoMT and deep learning in critical patient monitoring. We systematically examine existing research findings, elucidate the capabilities of IoMT-enabled SHC models, and address the challenges and opportunities inherent in their deployment.

Palabras llave : IoMT; SHC models; machine learning; deep learning; artificial intelligence.

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