Hybrid quantum-classical intelligence is a new promising paradigm to develop personalized smart healthcare systems with exemplification of computational power of quantum computing and scalability of classical artificial intelligent. This chapter introduces a hybrid design based on quantum machine learning, healthcare analytics, explainable AI, federated learning, and intelligent clinical decision support to apply precision medicine via this design. The given architecture will allow analyzing multimodal healthcare data efficiently, predicting the disease-risk, optimizing treatment plans on a personal scale, interpreting medical images, and predictive healthcare analytics. The framework enables the next-generation smart healthcare ecosystems by improving the computational efficiency, decision accuracy, transparency, and preserving privacy to enhance the realization of intelligent, patient-centric healthcare services.
Hybrid quantum-classical intelligence is a new promising paradigm to develop personalized smart healthcare systems with exemplification of computational power of quantum computing and scalability of classical artificial intelligent. This chapter introduces a hybrid design based on quantum machine learning, healthcare analytics, explainable AI, federated learning, and intelligent clinical decision support to apply precision medicine via this design. The given architecture will allow analyzing multimodal healthcare data efficiently, predicting the disease-risk, optimizing treatment plans on a personal scale, interpreting medical images, and predictive healthcare analytics. The framework enables the next-generation smart healthcare ecosystems by improving the computational efficiency, decision accuracy, transparency, and preserving privacy to enhance the realization of intelligent, patient-centric healthcare services.
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.
The integration of quantum computing and artificial intelligence (AI) holds transformative potential for healthcare by enabling adaptive diagnostic models that evolve with dynamic patient data and expanding medical knowledge. Leveraging quantum principles such as superposition and entanglement allows these models to efficiently process complex, high-dimensional healthcare datasets, improving precision in disease diagnosis and personalized treatment strategies. The proposed hybrid architecture combines quantum machine learning algorithms with classical AI components for interpretability and continuous refinement through real-time data ingestion, mitigating model drift and maintaining clinical relevance. This approach demonstrates significant promise in enhancing diagnostic accuracy and fostering intelligent healthcare solutions responsive to individual patient needs and medical advances [1]– [3].
Quantum-Driven Predictive Healthcare represents a pioneering integration of quantum computing principles with advanced risk modeling and artificial intelligence to revolutionize medical decision support systems. This approach harnesses quantum algorithms to process vast, complex healthcare datasets, enabling rapid identification of subtle patterns and correlations that traditional computing systems may overlook. By incorporating quantum-enhanced risk modeling, clinicians can benefit from more precise predictions of disease progression and patient outcomes, ultimately facilitating early intervention and personalized treatment strategies. AI-powered decision support further refines these predictions by dynamically adapting to new data inputs, learning continuously from real-time clinical feedback. The synergy between quantum computing and AI not only accelerates data analysis but also improves diagnostic accuracy and resource allocation within healthcare institutions. Moreover, the methodology promotes a deeper understanding of multifactorial health conditions by integrating diverse datasets—from genomics to electronic health records—into a coherent analytical framework. This fusion of technologies is poised to overcome the limitations of conventional statistical models, offering a robust, scalable, and future-proof solution for predictive healthcare. As healthcare challenges become increasingly complex, embracing quantum-driven solutions will be instrumental in transforming medical risk assessment and decision-making processes. The research presented herein lays the groundwork for next-generation clinical tools that merge computational innovation with practical healthcare applications, promising enhanced patient outcomes and more efficient medical care delivery.
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.
Advancements in quantum computing hold the potential to revolutionize artificial intelligence (AI), particularly in the field of healthcare. This paper explores how quantum computing can be leveraged to improve predictive analytics and facilitate personalized medicine. Through enhanced computational capacity, quantum computing enables faster processing and analysis of large, complex datasets, essential for predictive models in healthcare. This integration can lead to more precise diagnostics, treatment options, and disease prevention strategies by refining AI’s capability to handle vast data. Quantum algorithms such as quantum neural networks and quantum support vector machines can significantly optimize machine learning models, reducing the time required for data processing and improving predictive accuracy. Personalized medicine, an emerging trend in healthcare, relies heavily on detailed patient data to tailor treatments. The combination of quantum computing and AI promises to make this process faster, more accurate, and scalable. This study presents a detailed review of the current state of quantum AI in healthcare, highlights methodologies for data analysis, and showcases a comprehensive architecture for integrating quantum computing in AI healthcare systems. We analyse relevant data from case studies to demonstrate the advantages and challenges of adopting quantum computing for AI-driven healthcare applications.
This chapter develops an integrated framework for quantum-enhanced federated digital twins in Internet of Medical Things (IoMT)-based smart healthcare systems. It explains how continuous sensing, edge intelligence, federated learning, digital twin modeling, and near-term hybrid quantum-classical machine learning can be combined to support privacy-preserving predictive and personalized medicine. The chapter argues that the value of quantum-enhanced healthcare should not be framed as immediate quantum superiority, but as a disciplined pathway for testing quantum kernels, variational circuits, quantum-inspired optimization, and hybrid classifiers against strong classical baselines. It presents a layered architecture, clinical use cases, security safeguards, implementation challenges, and research directions. The chapter provides a clinically grounded and ethically cautious roadmap for researchers, healthcare AI developers, hospital leaders, and policymakers seeking trustworthy intelligent healthcare without centralizing sensitive patient data.