Modern healthcare depends on quick diagnoses, secure sharing of medical information, and smooth coordination among hospitals and research institutions. However, traditional computing systems often struggle to manage large and complex clinical data, advanced medical images, and collaboration across multiple organizations. Quantum computing, when combined with e-collaboration platforms, can overcome these challenges by delivering faster analysis, stronger security, and more scalable clinical intelligence. Core quantum principles such as superposition and entanglement—support parallel data processing, rapid optimization, and improved pattern detection in genomics, medical imaging, and continuous patient monitoring. Quantum-enabled backends can strengthen digital collaboration by supporting remote consultations, shared clinical reviews, and coordinated care. Secure communication, privacy-preserving analytics, biomedical simulation, and public-health preparedness are discussed, along with current limitations and future directions.
Modern healthcare depends on quick diagnoses, secure sharing of medical information, and smooth coordination among hospitals and research institutions. However, traditional computing systems often struggle to manage large and complex clinical data, advanced medical images, and collaboration across multiple organizations. Quantum computing, when combined with e-collaboration platforms, can overcome these challenges by delivering faster analysis, stronger security, and more scalable clinical intelligence. Core quantum principles such as superposition and entanglement—support parallel data processing, rapid optimization, and improved pattern detection in genomics, medical imaging, and continuous patient monitoring. Quantum-enabled backends can strengthen digital collaboration by supporting remote consultations, shared clinical reviews, and coordinated care. Secure communication, privacy-preserving analytics, biomedical simulation, and public-health preparedness are discussed, along with current limitations and future directions.
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 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.
The e-collaboration in the healthcare sector could be brought to revolutionary opportunities by Artificial Intelligence (AI) and Quantum Computing. With the world ever growing in digitization, support of secure, real-time as well as intelligent decisions has grown in demand. The paradigm hybrid described in the current paper employs the quantum-enhanced AI to support a seamless interaction among clinicians, patients, and administrators. The framework can be applied to accomplish faster diagnostics, precision medicine, and successful big health data management with the predictive analytics of AI and parallel processing in quantum computing. It presents the decentralized AI models, with quantum optimization, encryption, and resource scheduling algorithms. Interoperability, ethical issues, and quantum hardware constraints are the key issues. The promise of quantum telemedicine is reflected in such use cases as federated quantum learning, quantum-secure telemedicine, and AI-powered clinical decision support systems. According to our results, quantum-assisted AI e-collaboration is able to enhance patient outcomes, decrease errors, and transform healthcare.
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.
This chapter introduces the foundations of quantum computing and explains why it is poised to reshape modern healthcare. It outlines how quantum principles such as superposition and entanglement enable new forms of computation that can address the growing complexity of medical data, scientific discovery, and clinical decision-making. The chapter explores practical applications across drug discovery, personalized medicine, medical imaging, diagnostics, hospital operations, and data security, showing how quantum systems could solve challenges that exceed the limits of classical computing. Readers are guided through the evolving landscape of hardware, algorithms, workflow integration, and ethical considerations that will shape adoption in clinical and research environments. By the end of the chapter, readers will understand both the transformative potential and the practical constraints of quantum computing, gaining insight into how this emerging technology may support faster discovery, more precise care, stronger security, and more efficient healthcare systems.
The healthcare sector faces complex challenges that call for innovative solutions to improve diagnostic accuracy, treatment efficacy, and data management. Quantum computing, with its unique capabilities, holds the potential to revolutionize various aspects of healthcare. This narrative review critically examines the existing literature on the application of quantum computing in healthcare, focusing on its utility in enhancing diagnostics, data processing, and treatment planning. Quantum computing's ability to handle large, complex datasets more efficiently than classical computers can significantly impact domains such as genomics, medical imaging, and personalized medicine. Quantum algorithms can accelerate the identification of genetic markers associated with diseases, facilitate the analysis of medical images, and optimize treatment plans based on individual genetic profiles. Moreover, quantum cryptography offers a robust security solution for safeguarding sensitive patient data, a critical need as healthcare increasingly relies on digital platforms. Despite the promising outlook, the integration of quantum computing into healthcare faces technical, ethical, and regulatory challenges. The delicate nature of quantum hardware, the need for error correction, and the scalability of quantum systems pose barriers to widespread adoption. Additionally, concerns around patient privacy and data security, as well as the need for updated regulatory frameworks, must be addressed. Ongoing research and collaborative efforts involving researchers, healthcare providers, and technology developers are crucial to overcoming these hurdles and realizing the full potential of quantum computing in transforming healthcare. As quantum computing continues to evolve, its impact on the future of healthcare could be profound, leading to earlier disease detection, more personalized treatments, and improved patient outcomes. For instance, quantum computing has already been applied to enhance drug discovery processes, with companies like D-Wave Systems (Burnaby, Canada) demonstrating faster molecular simulations for pharmaceutical research and IBM's (Armonk, USA) quantum systems being used to model chemical reactions for new drug development.