Quantum computing and AI could change biomedical engineering by enabling smart biomedical applications. Quantum computing and artificial intelligence for smart biological applications are thoroughly explained in this chapter. Combining quantum computing with AI promotes personalised healthcare, disease diagnosis, and medication discovery. The authors begin with a hardware and software review of both systems before discussing AI and quantum computing. Quantum computing and AI in personalised healthcare are then considered. The authors discuss quantum computing and AI-powered telemedicine, precision medicine, and smart wearable and medical devices. They also note that reliable qubit technologies, effective error-correction methods, and scalable quantum computing and AI architectures are needed to fully exploit quantum computing and AI for smart biomedical applications. In conclusion, quantum computing and AI could revolutionise biomedical engineering and enable intelligent biomedical applications that improve patient outcomes.
Quantum computing and AI could change biomedical engineering by enabling smart biomedical applications. Quantum computing and artificial intelligence for smart biological applications are thoroughly explained in this chapter. Combining quantum computing with AI promotes personalised healthcare, disease diagnosis, and medication discovery. The authors begin with a hardware and software review of both systems before discussing AI and quantum computing. Quantum computing and AI in personalised healthcare are then considered. The authors discuss quantum computing and AI-powered telemedicine, precision medicine, and smart wearable and medical devices. They also note that reliable qubit technologies, effective error-correction methods, and scalable quantum computing and AI architectures are needed to fully exploit quantum computing and AI for smart biomedical applications. In conclusion, quantum computing and AI could revolutionise biomedical engineering and enable intelligent biomedical applications that improve patient outcomes.
The combination of Quantum Technologies and Artificial Intelligence (AI) is shaping a new approach in life sciences and healthcare. The growing complexity of biomedical data, along with the demand for quick and precise decision-making, has led to the exploration of new computing methods beyond traditional systems. AI has shown great success in medical diagnosis, disease prediction, drug discovery, and healthcare analytics. However, traditional AI models have drawbacks when it comes to optimization, scalability, and computational efficiency. Quantum technologies offer new computing principles based on quantum mechanics, allowing for parallel processing and better optimization. This review provides a detailed look at recent progress in quantum technologies and AI applications within life sciences and healthcare. The paper examines the basics of quantum computing, quantum-inspired algorithms, and hybrid quantum-AI frameworks, emphasizing their uses in disease diagnosis, medical imaging, genomics, molecular modeling, and drug discovery. It also discusses current challenges, practical limitations, and future research directions. This review aims to give researchers, students, and practitioners a clear understanding of the developing quantum-AI landscape and its potential effects on future healthcare systems.
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 integration of quantum computing and artificial intelligence represents a paradigm shift in the ability of medicine to solve complex challenges. Quantum computing's unparalleled processing power enables the simulation of molecular interactions, decoding of genomic data, and modeling of biological systems at a scale and speed unattainable by classical computers. When paired with advanced AI algorithms, this technological synergy accelerates drug discovery, enhances diagnostic precision, and enables the development of highly personalized therapies. Moreover, these technologies are poised to reduce the time and cost of drug development, improve patient outcomes, and facilitate real-time clinical decision-making. This chapter highlights the importance of promoting the joint application of quantum computing and artificial intelligence to solve complex medical problems. Despite the progress achieved to date, there are current challenges to achieving widespread applications and prospects for this research topic.
Quantum computing, an interdisciplinary area that processes data in fundamentally different ways, has recently grown and attracted a lot of attention from academics and businesses alike because of its potential to achieve computational powers that were previously unimaginable. Quantum computing's potential in the medical field has not been thoroughly explored as yet. Computing at the quantum level has the potential to revolutionize computationally intensive healthcare applications such as personalized medicine, drug discovery, medical imaging, DNA sequencing, and operational optimization. This review article provides the first all- encompassing examination of the many applications of quantum computing to healthcare systems, focusing on these specific domains. Many different areas are covered by our taxonomies, such as foundational and enabling technologies, applications, needs, architectures, security, open problems, and potential avenues for future study. The outcome of this thorough literature study is an all-encompassing perspective on the quantum computing paradigm as it pertains to healthcare. Synthetic intelligence (AI) and quantum computing are about to converge, and this article delves into how that can change the face of healthcare. In this study, we take a look at how quantum physics might help us understand how to improve AI for faster drug development, more precise clinical diagnostics, and more tailored treatment plans. The investigation probes the means by which molecular-level quantum simulations can represent complicated organic processes, enabling the unprecedented prediction of treatment interactions and the design of medicine. Also covered in the article is the idea of using quantum sensors in conjunction with AI to provide better clinical imaging that can pick up on more widespread problems. Highlighting its role in bringing in a new technology of personalized medicinal drug and efficient drug improvement, this synergy is discussed for its transformational power.