Interdisciplinary Perspectives on the Convergence of Healthcare, Artificial Intelligence, and Internet of Things

This study advocates integrating data from IoT and AI devices in health care to improve patient outcomes. Many Internet of Things (IoT) devices, such as smart tech and remote tracking systems, constantly collect health data. A rigorous cleaning framework ensures data quality. This framework reduces noise and boosts signals. We use standardization, outlier reduction, and feature extraction to create a solid training model collection. Change hyperparameters and use cross-validation to improve model training and ensure accurate predictions without overtraining the model. We measure model success by accuracy, precision, and memory. A continuous feedback process, based on success data and user participation, improves third-level estimations. The findings reveal that the recommended IoT -AI method measures performance better than conventional health care systems and AI -enhanced diagnostic systems. This systematic, data-driven approach may help healthcare personnel make better choices, enhance patient outcomes, and streamline and customize the healthcare delivery system. Technology is crucial for adjusting to patient care demands in today's health care system, as shown by this research.