Advancing healthcare through real-time AI diagnostics: Current innovations and applications

Pedada, Naveen Kumar (2025) Advancing healthcare through real-time AI diagnostics: Current innovations and applications. World Journal of Advanced Engineering Technology and Sciences, 15 (1). pp. 1250-1254. ISSN 2582-8266

The integration of Artificial Intelligence and Machine Learning in healthcare diagnostics has revolutionized patient care through real-time analysis and decision support capabilities. These technologies have transformed medical imaging interpretation, enabled continuous physiological monitoring through wearable devices, and enhanced clinical decision-making processes. Deep learning architectures, particularly Convolutional Neural Networks and Recurrent Neural Networks, have demonstrated exceptional capabilities in detecting abnormalities across various imaging modalities and analyzing temporal health data. The implementation of edge computing has significantly improved data processing speeds at the point of care, while automated segmentation algorithms have enhanced the precision of anatomical delineation. This technological advancement has particularly benefited emergency medicine and critical care settings, where rapid and accurate diagnostics can significantly impact patient outcomes. The evolution of these systems continues to improve healthcare delivery through enhanced accuracy, efficiency, and accessibility of diagnostic services.