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 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.
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.
Artificial Intelligence (AI) and Quantum Computing together are going to transform medical research, diagnostics and the simulation of treatment. Although it has already been applied to medical imaging, drug discovery and predictive analytics, AI needs large amounts of computations that are sometimes beyond the capacity of more traditional computing. Quantum computers due to their capability to do parallel processing over quantum states can be fast and efficient in unprecedented ways. The paper will discuss the combination of AI and quantum computing in the medical field, including the characterization of medical applications of AI where it complements quantum computing, an overview of the current medical applications of AI and quantum computing, and real-time diagnostics, genomics, drug discovery, molecular dynamics, and personalized medicine. We discuss the current issues state-of-art including quantum error correction, interpretability of models, and ethical implications and point toward future opportunities including digital twins, federated learning using quantum resources, and hybrid quantum-classical models. The eventuality of this integration might end up speeding up medical simulations, decreasing the expense of drug development and enhancing the patient outcomes across the globe.
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.
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.
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.