The healthcare sector is undergoing a significant transformation driven by advances in Artificial Intelligence, Big Data Analytics, Internet of Medical Things, Cloud Computing, and Digital Health technologies. While these technologies have enabled intelligent healthcare services, the increasing complexity, volume, and heterogeneity of healthcare data pose substantial computational challenges for conventional systems. To address these limitations, Quantum Computing, has emerged as a promising solution. This chapter presents a comprehensive review of the integration of quantum computing and intelligent technologies in smart healthcare systems. It examines the fundamental concepts of quantum computing and explores their applicability in healthcare environments. It also discusses quantum machine learning models for diagnostic support and personalized healthcare. It further investigates emerging applications including drug discovery, genomic analysis and medical image reconstruction. Finally, the challenges, implementation barriers, and future directions are also presented.
The healthcare sector is undergoing a significant transformation driven by advances in Artificial Intelligence, Big Data Analytics, Internet of Medical Things, Cloud Computing, and Digital Health technologies. While these technologies have enabled intelligent healthcare services, the increasing complexity, volume, and heterogeneity of healthcare data pose substantial computational challenges for conventional systems. To address these limitations, Quantum Computing, has emerged as a promising solution. This chapter presents a comprehensive review of the integration of quantum computing and intelligent technologies in smart healthcare systems. It examines the fundamental concepts of quantum computing and explores their applicability in healthcare environments. It also discusses quantum machine learning models for diagnostic support and personalized healthcare. It further investigates emerging applications including drug discovery, genomic analysis and medical image reconstruction. Finally, the challenges, implementation barriers, and future directions are also presented.
Quantum-Enhanced Artificial Intelligence (QAI) integrates quantum computing with advanced AI to tackle the increasing complexity of healthcare data. The chapter discusses the impact of QAI in medical diagnostics, precision medicine, drug discovery and clinical decision support systems. It also explores the use cases in the Internet of Medical Things (IoMT), digital twins, and predictive healthcare analytics. In addition, current challenges, ethical issues and future directions are identified, illustrating the possibilities of QAI to empower next-generation intelligent medical systems to provide faster, more accurate and personalized healthcare.
Quantum computing with AI-driven medical technologies represents an absolute frontier of transformation in precision medicine, diagnostics, and drug discovery. This chapter explores the intersection of quantum computing and artificial intelligence in transforming complex medical challenges, namely in big data analysis, acceleration in molecular simulations, and personalizing treatment plans. Quantum algorithms can scale exponentially beyond AI's capabilities in the breakthrough of genomic analysis, radiological interpretation, and predictive modeling for patient outcomes. The focus of the chapter will be on state-of-the-art achievements in quantum machine learning, possible applications in healthcare areas, and their future prospects for the revolutionizing of medical imaging, optimization of clinical trials, and real-time data processing on wearable devices. They confront the computational limits of classical systems and offer new ground for significant progress in both medical research and health care delivery by solving problems that have until now been intractable.
Quantum intelligence, which integrates quantum computing and artificial intelligence, is emerging as a transformative approach for personalized and precision medicine. This chapter explores its applications in genomic analysis, disease prediction, drug discovery, and clinical decision-making. Quantum machine learning can efficiently process complex healthcare datasets, enabling improved risk prediction, patient stratification, and personalized treatment. The chapter highlights applications in cancer care, cardiovascular disease prediction, rare disease diagnosis, and smart healthcare systems integrating electronic health records and Internet of Medical Things devices. It also discusses challenges related to data security, privacy, ethics, and regulatory readiness. Overall, quantum intelligence has strong potential to improve the accuracy, efficiency, and personalization of future healthcare.
Abstract Quantum computing and artificial intelligence (AI) are converging to reshape the future of healthcare. Modern medicine generates vast and complex datasets from imaging, genomics, wearable sensors, and real-time clinical records. While AI has already demonstrated significant potential in diagnostics, drug discovery, and personalised medicine, its progress is often constrained by high computational demand, limited interpretability, and energy inefficiency. Quantum computing offers a fundamentally different paradigm, using qubits, superposition, and entanglement to process high-dimensional data and explore solution spaces in parallel. This synergy-quantum-enhanced AI or quantum machine learning (QML)promises transformative capabilities across the healthcare continuum. This narrative review explores the foundations of quantum computing, the evolution of AI in medicine, and the points where these technologies intersect. Applications span a range of biomedical domains. In radiology and pathology, quantum-assisted classifiers can enhance both the speed and interpretability of image analysis. In drug discovery and genomics, variational quantum eigensolvers and hybrid quantum–classical models may accelerate molecular simulations and genome-wide association studies. In personalized medicine, quantum kernel methods enable more precise phenotype clustering and improve the accuracy of treatment response predictions. Operationally, quantum-AI integration also supports hospital logistics, ICU resource allocation, and supply-chain resilience. Despite these opportunities, significant challenges remain, including hardware limitations, data encoding bottlenecks, interpretability deficits, and regulatory ambiguity. Ethical concerns around privacy, equity, and compliance with frameworks such as HIPAA and GDPR must also be addressed. By mapping opportunities and risks, this review underscores that quantum-AI is no longer speculative but an emerging reality. Harnessing its potential requires multidisciplinary collaboration to translate qubits and algorithms into tangible improvements in patient outcomes, clinical trust, and system efficiency.
The convergence of quantum computing and artificial intelligence (AI) presents a paradigm shift in healthcare, revolutionizing complex biological simulations, genomic data processing, and drug discovery innovations. Traditional computational methods, despite their advancements, often struggle with the sheer scale and complexity of biological data, limiting the speed and accuracy of medical breakthroughs. Quantum computing, with its ability to process vast datasets exponentially faster than classical computers, coupled with AI's predictive capabilities, offers a transformative solution for accelerating biomedical research and clinical applications. This paper explores quantum machine learning’s role in optimizing AI-driven molecular dynamics simulations for drug discovery. By leveraging quantum-enhanced algorithms, researchers can rapidly model molecular interactions, analyze drug-receptor binding affinities, and predict pharmacokinetics with unprecedented precision. Additionally, we examine quantum-assisted deep learning models for deciphering intricate biological mechanisms such as protein folding, epigenetic modifications, and metabolic pathway interactions, enabling more accurate predictions of disease progression and therapeutic targets. Furthermore, the integration of AI-quantum hybrid models in clinical diagnostics and imaging analytics is redefining personalized medicine. Quantum-enhanced deep learning facilitates high-resolution medical imaging, real-time anomaly detection, and optimized radiomic feature extraction, leading to early and more accurate disease diagnosis. In genomics, quantum computing significantly accelerates whole-genome sequencing and mutation analysis, paving the way for tailored treatment strategies based on an individual's genetic profile. Despite its promise, challenges such as quantum hardware limitations, data coherence issues, and ethical considerations must be addressed to ensure the practical implementation of quantum-AI healthcare solutions. This paper provides a comprehensive analysis of the potential, challenges, and future directions of quantum-AI synergy in transforming modern healthcare.
Transformative impact and change can be acquired by quantum computing in the field of medical sciences and clinical care. This chapter explores the revolutionary prospects of quantum physics, that is, superposition and entanglement, with its application in medical sciences and healthcare. Additionally, a comprehensive framework of the technology is presented in this chapter in the facets of quantum artificial intelligence applications, drug discovery, personalized treatment, biomarker discovery, and data management. The chapter examines the concepts from quantum computing to advanced healthcare from next-generation applications. Despite a transforming potential, challenges due to technical complexities, ethical and regulatory concerns, high cost, and limited accessibility from the unification of quantum computing with healthcare are also presented. A practical roadmap is discussed at the end of the chapter to integrate multidisciplinary technologies for creating smart quantum healthcare solutions. Actionable insights are offered in the section for healthcare and biomedical scientists, healthcare providers, pharmaceutical and biotechnological companies, public health management, and startup companies to dive into the possibilities that quantum computing can provide in healthcare.