Harnessing AI, Deep Learning and Quantum Machine Learning for Healthcare driven applications: A Systemic overview

Abstract

The convergence of Artificial Intelligence (AI), Deep Learning (DL), and Quantum Machine Learning (QML) is ushering in a transformative era in healthcare, enabling the development of highly intelligent, data-driven,
and predictive systems capable of augmenting clinical decision-making, enhancing diagnostic accuracy, and accelerating therapeutic discovery. This paper presents a comprehensive systemic overview of the theoretical
foundations, computational architectures, and practical implementations of AI, DL, and QML in healthcare driven applications. It delineates the current capabilities and constraints of classical AI and DL models in areas such as medical imaging, electronic health record (EHR) analytics, and precision medicine, while elucidating how QML offers a paradigm shift by harnessing quantum computational advantages to address the scalability, optimization, and feature extraction challenges inherent to classical approaches. Furthermore, this study critically examines state-of-the-art frameworks, algorithmic innovations, and hybrid quantum classical systems deployed in clinical and biomedical contexts. By synthesizing recent advancements, we highlight emerging trends, potential breakthroughs, and the interdisciplinary challenges that must be addressed for the scalable and ethical integration of intelligent systems in real-world healthcare ecosystems.