Introduction Quantum technologies, driven by principles of quantum mechanics like superposition and entanglement, have shown transformative potential in health and medicine. However, the understanding of these technologies in early to late stages of development is limited. This study aimed to investigate the emerging applications of quantum technologies in health care, with a focus on quantum computing, diagnostics, and therapeutics. Methods Horizon scanning researchers, in collaboration with information specialists and with steering from subject matter experts at National Health Service England, developed search strategies for identifying relevant emerging and established quantum applications in healthcare domains such as drug design, personalized medicine, diagnosis assistance, and treatment or interventions. We conducted a comprehensive review of this landscape by analyzing data from clinical trials, published literature, and soft intelligence sources. Primary and secondary sources were reviewed systematically using a combination of traditional methods and the Innovation Observatory’s in-house semi-automated Search Companion for Advanced News Article Retrieval (SCANAR) tool to capture non-traditional soft intelligence sources. Results The scan identified 116 quantum technologies in health care, with magnetoencephalography (MEG), quantum dots, and superconducting quantum interference devices (SQUIDs) being the most frequently used for brain mapping, imaging, and cardiac diagnostics. Over half (54%) of these technologies assist in diagnosis, while others are used in drug design (15%), treatment (15%), and precision medicine (13%). Quantum computing applications (28%) and quantum dots (24%) were the most common applications identified. Non-commercial trials dominated (88%), reflecting a focus on advancing care. Additionally, 27 percent of the technologies incorporate artificial intelligence, mainly for personalized medicine and imaging diagnosis. Conclusions Quantum technologies in health care are advancing areas of diagnostics, personalized medicine, and therapeutics. Diagnostics are the most popular area, most frequently using technologies such as MEG, SQUIDs, and quantum dots. Quantum computing algorithms accelerate drug discovery and precision medicine. Although promising, integrating quantum technologies into health care faces challenges in infrastructure demands and regulatory frameworks, and requires professional training in quantum literacy.Introduction Quantum technologies, driven by principles of quantum mechanics like superposition and entanglement, have shown transformative potential in health and medicine. However, the understanding of these technologies in early to late stages of development is limited. This study aimed to investigate the emerging applications of quantum technologies in health care, with a focus on quantum computing, diagnostics, and therapeutics. Methods Horizon scanning researchers, in collaboration with information specialists and with steering from subject matter experts at National Health Service England, developed search strategies for identifying relevant emerging and established quantum applications in healthcare domains such as drug design, personalized medicine, diagnosis assistance, and treatment or interventions. We conducted a comprehensive review of this landscape by analyzing data from clinical trials, published literature, and soft intelligence sources. Primary and secondary sources were reviewed systematically using a combination of traditional methods and the Innovation Observatory’s in-house semi-automated Search Companion for Advanced News Article Retrieval (SCANAR) tool to capture non-traditional soft intelligence sources. Results The scan identified 116 quantum technologies in health care, with magnetoencephalography (MEG), quantum dots, and superconducting quantum interference devices (SQUIDs) being the most frequently used for brain mapping, imaging, and cardiac diagnostics. Over half (54%) of these technologies assist in diagnosis, while others are used in drug design (15%), treatment (15%), and precision medicine (13%). Quantum computing applications (28%) and quantum dots (24%) were the most common applications identified. Non-commercial trials dominated (88%), reflecting a focus on advancing care. Additionally, 27 percent of the technologies incorporate artificial intelligence, mainly for personalized medicine and imaging diagnosis. Conclusions Quantum technologies in health care are advancing areas of diagnostics, personalized medicine, and therapeutics. Diagnostics are the most popular area, most frequently using technologies such as MEG, SQUIDs, and quantum dots. Quantum computing algorithms accelerate drug discovery and precision medicine. Although promising, integrating quantum technologies into health care faces challenges in infrastructure demands and regulatory frameworks, and requires professional training in quantum literacy.

International Journal of Technology Assessment in Health CareDec 1, 20250 citations

Challenges in implementing quantum technology in healthcare systems

Kali Charan Rath, Alex Khang

Elsevier eBooksOct 25, 20251 citation

Privacy-Preserved, Provable Secure, Mutually Authenticated Key Agreement Protocol for Healthcare in a Smart City Environment

Shaheena Khatoon, Sk. Md. Mizanur Rahman, Majed Alrubaian +1 more

Smart home systems can provide health care services for people with special needs in their own homes. Briefly defined, such a smart home has special electronics to enable the remote control of automated devices specifically designed for remote health care to ensure the safety of the patient at home and the supervision of their health status. These sensors are linked to a local intelligence unit responsible for analyzing sensor data, detecting emergency situations, and interfacing between the patient at home and a set of people involved in their health care, such as doctors, nurses, emergency services, and paramedics. Smart homes can improve the patient's quality of life and safety through the innovative use of advanced technologies. Telemedicine and telecare are driving forces behind the adoption of smart homes. The telecare medicine information system (TMIS) has drawn worldwide attention for the past 20 years, as modern technologies have made remote delivery of healthcare a reality. TMIS using multidisciplinary research and application involves advanced technologies in information processing, telecommunications, bio-sensing, and artificial intelligence including smart technologies. TMIS leverages the latest mobile and wireless communication technologies and widely available internet infrastructure to deliver quality services to home patients enabling them to remotely access information about their health and obtain telemedical services. TMIS delivers capabilities to remotely provide $24\times 7$ health care facilities to patients. Its purpose is to provide patients with convenient and expedited remote health care services, greatly improving the quality and efficiency of health care services. However, the open and insecure nature of the internet poses a number of security threats to patient secrecy and privacy. Security design for TMIS is not trivial. Essential security and privacy are provided by mutual authentication and key agreement protocols. This paper proposes an efficient and secure, bilinear pairing-based, unlink-able, mutual authentication and key agreement protocol for TMIS. The proposed protocol adopts a fuzzy extractor for the identification of patients using the biometric data. The security of the proposed protocol is based on the hardness of the elliptic curve discrete logarithm problem (ECDLP) and elliptic curve computational Diffie-Hellman problem (ECCDHP) to preserve the privacy of the user. The detailed security analysis is discussed, and the results of comparison are provided.Smart home systems can provide health care services for people with special needs in their own homes. Briefly defined, such a smart home has special electronics to enable the remote control of automated devices specifically designed for remote health care to ensure the safety of the patient at home and the supervision of their health status. These sensors are linked to a local intelligence unit responsible for analyzing sensor data, detecting emergency situations, and interfacing between the patient at home and a set of people involved in their health care, such as doctors, nurses, emergency services, and paramedics. Smart homes can improve the patient's quality of life and safety through the innovative use of advanced technologies. Telemedicine and telecare are driving forces behind the adoption of smart homes. The telecare medicine information system (TMIS) has drawn worldwide attention for the past 20 years, as modern technologies have made remote delivery of healthcare a reality. TMIS using multidisciplinary research and application involves advanced technologies in information processing, telecommunications, bio-sensing, and artificial intelligence including smart technologies. TMIS leverages the latest mobile and wireless communication technologies and widely available internet infrastructure to deliver quality services to home patients enabling them to remotely access information about their health and obtain telemedical services. TMIS delivers capabilities to remotely provide $24\times 7$ health care facilities to patients. Its purpose is to provide patients with convenient and expedited remote health care services, greatly improving the quality and efficiency of health care services. However, the open and insecure nature of the internet poses a number of security threats to patient secrecy and privacy. Security design for TMIS is not trivial. Essential security and privacy are provided by mutual authentication and key agreement protocols. This paper proposes an efficient and secure, bilinear pairing-based, unlink-able, mutual authentication and key agreement protocol for TMIS. The proposed protocol adopts a fuzzy extractor for the identification of patients using the biometric data. The security of the proposed protocol is based on the hardness of the elliptic curve discrete logarithm problem (ECDLP) and elliptic curve computational Diffie-Hellman problem (ECCDHP) to preserve the privacy of the user. The detailed security analysis is discussed, and the results of comparison are provided.

IEEE AccessJan 1, 201989 citations

Quantum Computing in Healthcare: A New Frontier

Kinjal Gandhi, Dweepna Garg, Milind Shah +2 more

Smart sensors, measurement and instrumentationJan 1, 20261 citation

24\times 7

health care facilities to patients. Its purpose is to provide patients with convenient and expedited remote health care services, greatly improving the quality and efficiency of health care services. However, the open and insecure nature of the internet poses a number of security threats to patient secrecy and privacy. Security design for TMIS is not trivial. Essential security and privacy are provided by mutual authentication and key agreement protocols. This paper proposes an efficient and secure, bilinear pairing-based, unlink-able, mutual authentication and key agreement protocol for TMIS. The proposed protocol adopts a fuzzy extractor for the identification of patients using the biometric data. The security of the proposed protocol is based on the hardness of the elliptic curve discrete logarithm problem (ECDLP) and elliptic curve computational Diffie-Hellman problem (ECCDHP) to preserve the privacy of the user. The detailed security analysis is discussed, and the results of comparison are provided.