Healthcare powered by artificial intelligence is revolutionising the way patients are treated: AI helps diagnose intelligently and lets one manage data intelligently. Nevertheless, this is under threat with the emergence of quantum computing, which threatens to crack all established cryptographic protection in these systems. Currently, this study aims to explore how ready AI-powered healthcare security systems are to counter attacks based on quantum computing. It discusses present vulnerabilities, and considerations of post-quantum cryptographic protections and provides quantum resilience strategic measures. Based on secondary data analysis, literature review, and case studies, the study highlights the major gaps and offers a road map to make safe AI applications in healthcare. Research results underline the importance of implementing quantum-resistant technologies in the short term to maintain long-term data integrity and protection against the system.
Healthcare powered by artificial intelligence is revolutionising the way patients are treated: AI helps diagnose intelligently and lets one manage data intelligently. Nevertheless, this is under threat with the emergence of quantum computing, which threatens to crack all established cryptographic protection in these systems. Currently, this study aims to explore how ready AI-powered healthcare security systems are to counter attacks based on quantum computing. It discusses present vulnerabilities, and considerations of post-quantum cryptographic protections and provides quantum resilience strategic measures. Based on secondary data analysis, literature review, and case studies, the study highlights the major gaps and offers a road map to make safe AI applications in healthcare. Research results underline the importance of implementing quantum-resistant technologies in the short term to maintain long-term data integrity and protection against the system.
Cloud computing has emerged as the prevailing development in healthcare systems across the global market. These systems are creating and processing vast amounts of patient data that require a certain level of security. However, due to the introduction of quantum computation, the future of cryptographic techniques on which Cloud security relies is in danger. The following paper seeks to explore the compatibility of quantum computing and cloud security and regard to the protection of health data. It also includes a comprehensive analysis of the current risks, a discussion of already existing quantum-vulnerable points, and a strategy for creating a quantum-safe strategy for safe patient data storage in healthcare. The study under consideration also employs quantum cryptography and cloud structures to identify threats and create appropriate defence mechanisms. Some models explored and analyzed include Quantum Key Distribution (QKD), Post-Quantum Cryptography (PQC) and hybrid cryptosystems. A simulated hospital database has brought about the fragility of some of these algorithms, a research work dubbed as quantum resilience, in order to explain how it is possible to integrate these two concepts without removing the aspects of the cloud that make it appealing to many people, including scalability and accessibility. This indicates that there has been a major enhancement in standing against quantum attacks, specifically showing the way towards effective, sustainable and protected healthcare information systems.
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.
In the field of healthcare, the development of artificial intelligence (AI) gives a tremendous opportunity for improving patient care and medical diagnostics. However, the safety of these artificial intelligence systems continues to be a major worry. There is a possibility that traditional encryption methods will not be able to withstand advanced cyber threats. This research proposes to improve the safety of artificial intelligence systems in the healthcare industry by utilizing the power of Quantum networks. Because quantum networks are based on the principles of quantum mechanics, which are comparable to those of traps and superpositions, they provide security in previously unforeseen conditions. It is possible to have sensitive case data transferred securely by implementing Quantum communication protocols into healthcare AI systems. This will protect the data from being intercepted and from being accessed by unwanted parties. When it comes to protecting AI operations in the healthcare industry, ensure patient sequestration, and the integrity of medical data.
With the rapid advancement of artificial intelligence (AI) and quantum computing, cybersecurity threats have evolved, giving rise to adversarial quantum attacks. These attacks exploit the vulnerabilities of AI models using quantum algorithms, posing a significant risk to data security, model robustness, and decision-making systems. This paper presents a comprehensive review of adversarial quantum attacks on AI, analyzing their mechanisms, potential impacts, and countermeasures. It explores how quantum computing can enhance adversarial attacks by accelerating the generation of adversarial examples, breaking cryptographic protections, and undermining AI model integrity. Additionally, the study examines different attack vectors, including quantum-enhanced adversarial perturbations, quantum machine learning (QML) vulnerabilities, and quantum decryption threats. The paper also discusses defensive strategies such as quantum-resistant AI models, quantum cryptographic defenses, and hybrid quantum-classical security frameworks that can mitigate these risks. By evaluating existing research and emerging trends, this review provides insights into the growing intersection of AI security and quantum computing. The findings emphasize the urgent need for robust quantum-aware AI security frameworks to safeguard AI-driven systems in the quantum era. Future research directions include developing quantum-adaptive AI models, post-quantum cryptographic techniques, and real-world applications of quantum-safe AI architectures. This review aims to contribute to the ongoing discourse on ensuring AI resilience against adversarial quantum threats, paving the way for a secure and quantum-resistant future.
As artificial intelligence (AI) systems increasingly underpin critical applications in healthcare, finance, defense, and infrastructure, ensuring their security has become paramount. However, the rapid advancement of quantum computing poses a significant threat to classical cryptographic schemes that currently safeguard these systems. Quantum algorithms such as Shor’s and Grover’s threaten to undermine the confidentiality, integrity, and authentication of data and models that AI systems depend on. In this context, Post-Quantum Cryptography (PQC) has emerged as a crucial area of research aimed at developing cryptographic algorithms that are secure against both classical and quantum adversaries. This paper investigates the intersection of quantum computing threats and AI system vulnerabilities, highlighting how current security mechanisms may fail in a post-quantum era. We explore various categories of PQC—including lattice-based, hash-based, and multivariate polynomial cryptography—and evaluate their applicability to securing AI systems, particularly in scenarios such as model distribution, federated learning, data encryption, and communication in AI-enabled edge and IoT environments. Through analysis of emerging use cases and proposed architectures, the paper outlines how PQC can be integrated into the AI lifecycle to mitigate quantum-era threats. It also identifies key challenges such as computational overhead, integration complexity, and standardization gaps. The study concludes by outlining future research opportunities, including the development of efficient PQC protocols tailored to AI workloads, the creation of quantum-resilient AI pipelines, and the importance of interdisciplinary collaboration to secure AI’s future.
The increasing digitalization of the healthcare system, driven by electronic health records, medical IoT devices, and AI-aided diagnostics, has significantly expanded the attack surface for cyber threats. Although current cryptographic protocols provide sufficient security against classical adversaries, new developments in quantum computing threaten the confidentiality, integrity, and availability of medical information as never before. Shor’s algorithm could break widely used asymmetric encryption systems, and Grover’s algorithm weakens symmetric ones, which could render modern healthcare security systems obsolete. This review provides an in-depth and organized analysis of the evolving quantum threat environment in healthcare ecosystems and of the need to move toward quantum-resilient security architectures. It synthesizes the existing literature on post-quantum cryptography (PQC) and identifies lattice-based, hash-based, multivariate, and code-based algorithms as potentially viable long-term replacements. Moreover, it examines the cybersecurity issues that quantum-enabled attacks pose to medical IoT systems, clinical decision-support systems, wearable sensors, telemedicine systems, and cloud-based health information exchanges. The review also covers hybrid quantum-classical defenses, quantum key distribution (QKD), quantum random number generation (QRNG), and blockchain-based trust systems that aim to make systems resilient to advanced persistent quantum attacks. This paper identifies key gaps and outlines future research directions for developing quantum-secure medical systems. The results highlight the importance of early adoption of PQC standards and a layered cyber defense approach to protect patient privacy, ensure regulatory compliance, and retain trust in next-generation digital medical data communication.