The growing amount of data obtained from electronic health records, medical imaging systems, wearable devices, genetic data banks and the Internet of Medical Things is making decision making in healthcare settings, medical analytics challenging and data safety and security difficult. A novel paradigm known as Quantum-Enhanced Intelligent Healthcare Framework is presented. It not only merges Artificial Intelligence and Quantum Machine Learning, but also integrates Explainable Artificial Intelligence, Blockchain technology, Differential Privacy and Post-Quantum Cryptography in one Healthcare Intelligence Ecosystem. The proposed framework involves using the classical AI models such as CNN, LSTM, Random Forest, and XGBoost. When quantum analytics is used in more complex contexts, such as in the health sector, quantum support vector machines, variational quantum classifiers and quantum neural networks are used. The explainability capacities of SHAP and LIME make it attainable to boost clinical trust and CRYSTALS-Kyber and CRYSTALS-Dilithium would help safeguard Quantum PC data.
The growing amount of data obtained from electronic health records, medical imaging systems, wearable devices, genetic data banks and the Internet of Medical Things is making decision making in healthcare settings, medical analytics challenging and data safety and security difficult. A novel paradigm known as Quantum-Enhanced Intelligent Healthcare Framework is presented. It not only merges Artificial Intelligence and Quantum Machine Learning, but also integrates Explainable Artificial Intelligence, Blockchain technology, Differential Privacy and Post-Quantum Cryptography in one Healthcare Intelligence Ecosystem. The proposed framework involves using the classical AI models such as CNN, LSTM, Random Forest, and XGBoost. When quantum analytics is used in more complex contexts, such as in the health sector, quantum support vector machines, variational quantum classifiers and quantum neural networks are used. The explainability capacities of SHAP and LIME make it attainable to boost clinical trust and CRYSTALS-Kyber and CRYSTALS-Dilithium would help safeguard Quantum PC data.
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.
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.
The growing volume of biomedical data from electronic health records, medical imaging, genomic sequencing, wearable Internet of Medical Things (IoMT) devices, and precision medicine has created new opportunities as well as significant computational challenges for healthcare. This chapter explores how Quantum Machine Learning (QML) can complement conventional machine learning by improving the analysis of complex, high-dimensional healthcare data through hybrid quantum–classical approaches. It introduces the fundamentals of quantum computing and discusses their application in genomics, medical imaging, drug discovery, IoMT-enabled healthcare, and cybersecurity. The chapter also presents an integrated framework that brings together quantum-enabled analytics, secure communication, and clinical decision support, while highlighting current challenges, ethical considerations, and future directions such as fault-tolerant quantum computing, explainable quantum AI, federated quantum learning, and post-quantum healthcare security.
Background Artificial Intelligence (AI) incorporation within healthcare applications has significantly helped in the improvement of healthcare diagnostics, patient forecasting, and clinical decision-making. The steady growing of medical data in terms of quantity, quality, and dimension poses more significant problems for traditional AI methods in terms of size, time, and computational efficiency. However, classical machine learning models often struggle with high-dimensional, imbalanced, and heterogeneous medical datasets, which limits their scalability and generalizability. The Quantum Artificial Intelligence (QAI) model, which combines quantum computing and machine learning in a complementary way, has emerged as one of the main trends to defeat these problems. This complementary approach is gaining more popularity, with the most advanced work being developed in this area, as it offers a fundamental explanation of current complexities and the underlying principles and their applications. Methodology This paper begins with the basic concepts of quantum computing and where it overlaps with AI. It then moves to a comparative approach, discussing various quantum machine learning algorithms such as quantum support vector machines, quantum neural networks, and variational quantum circuits. A comparative evaluation of quantum support vector machine, variational quantum classifier, quantum neural network, quantum K-means, and quantum Boltzmann machine is conducted using the Pima Indians Diabetes dataset to assess model behaviour and performance under standard metrics. Further detail is provided on the theoretical and practical implications of QAI in medical imaging, genomics, electronic health records, and drug discovery. Additionally, this paper examines platforms, tools, and quantum hardware used for QAI research, and discusses unique challenges currently faced in quantum technology. Results Unlike prior surveys that primarily provide conceptual overviews, this paper uniquely combines a comprehensive review with practical benchmarking of multiple quantum machine learning models on a benchmark medical dataset. This provides comparative insights into algorithmic performance under real-world constraints, particularly in relation to high-dimensional medical data, genomic data, electronic health records with large number of clinical attributes, radiology images, etc. In this study, dimensionality arises from multivariate clinical features encoded into quantum feature maps and the limitations of current quantum systems. Conclusion This paper aims to serve as a starting point for researchers and practitioners interested in the potential of quantum-enhanced AI to transform health data analytics. By identifying current limitations, evaluating the performance of emerging quantum machine learning approaches, and outlining gaps in the literature, the paper highlights future research directions necessary to advance QAI in healthcare. Overall, the findings outline both the potential and practical constraints of quantum models, offering a foundation for future work toward scalable, clinically applicable QAI solutions.
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.
Because many modern healthcare security systems rely on traditional cryptographic primitives that are susceptible to quantum attacks, the quick development of quantum computing poses previously unheard-of risks. With the increasing use of wearable sensors, telemedicine, AI-driven diagnostics and distributed clinical databases, as well as the increasing digitisation of healthcare, the need for quantum-resilient protection becomes critical. The integration of quantum and post-quantum cryptography (PQC) as fundamental security measures for upcoming intelligent healthcare ecosystems is thoroughly examined in this chapter. We summarise recent developments in cryptographic standards, secure communication protocols, AI-enabled defence mechanisms and blockchain-based data integrity systems using a PRISMA-guided systematic review. With an emphasis on performance, scalability and resistance to Shor- and Grover-based attacks, we further develop a simulation framework to assess hybrid quantum, classical security architectures under adversarial quantum scenarios. Our results show that while hash-based signatures provide ultra-long-term integrity for genomic and archival medical data, lattice-based PQC schemes, in particular CRYSTALS-Kyber and Dilithium, show strong suitability for electronic health record (EHR) encryption, secure IoMT communication and federated learning aggregation. A unified quantum-resilient architecture incorporating PQC, blockchain consensus, AI-based anomaly detection and quantum key distribution (QKD) is one of the theoretical contributions. In summary, we highlight the potential of quantum homomorphic AI, neuromorphic cryptosystems, quantum biometrics and adaptive threat intelligence to develop a safe, reliable and scalable paradigm for quantum-ready healthcare.