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Elsevier eBooks

Quantum artificial intelligence for human health: A statistical framework

Hossein Hassani, Leila Marvian Mashhad, Steve MacFeely

Published Jan 1, 2026• 0 citations
Publisher
DOI
10.1016/b978-0-443-43934-6.00039-0

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With the advent of Healthcare 4.0, there is increased interest from researchers the world over in the application of modern, cutting-edge Artificial Intelligence (AI) and Quantum Artificial Intelligence (QAI) algorithms in solving healthcare challenges. The era of Quantum Computing (QC) promises to bring significant advancements in several areas of healthcare such that it may be sensible to give this hybrid Quantum/Classical paradigm its own name-Healthcare4Q. The potential of QC will extend the reach of Healthcare4Q with the help of diverse technologies such as quantum-enabled wearables, quantum-secure transfer and storage of data, and quantum computing at edge, fog, and cloud. All of these technologies promise to catapult Healthcare4Q to become the most capable healthcare framework in the advancement of medical innovations and improvement of patient care. An integral part of a person's health lies in cardiovascular health, and thus prioritizing and optimizing cardiovascular health remains vital to the broader goals of public health and healthcare sustainability. In this study, under the paradigm of Healthcare4Q, we propose a framework called the Quantum AIdriven Heart Health Framework (QAIHHF) that can provide advanced predictive intelligence to healthcare providers by utilizing historical and real-time data and processing capabilities proposed in Healthcare4Q. We show that when applied to various diagnostics and health indicators such as ECG data, the Quantum AI provides accuracy at a level equal to or higher as compared to the classical methods thus proving itself to be the critical component that will herald the era of Healthcare4Q.

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With the advent of Healthcare 4.0, there is increased interest from researchers the world over in the application of modern, cutting-edge Artificial Intelligence (AI) and Quantum Artificial Intelligence (QAI) algorithms in solving healthcare challenges. The era of Quantum Computing (QC) promises to bring significant advancements in several areas of healthcare such that it may be sensible to give this hybrid Quantum/Classical paradigm its own name-Healthcare4Q. The potential of QC will extend the reach of Healthcare4Q with the help of diverse technologies such as quantum-enabled wearables, quantum-secure transfer and storage of data, and quantum computing at edge, fog, and cloud. All of these technologies promise to catapult Healthcare4Q to become the most capable healthcare framework in the advancement of medical innovations and improvement of patient care. An integral part of a person's health lies in cardiovascular health, and thus prioritizing and optimizing cardiovascular health remains vital to the broader goals of public health and healthcare sustainability. In this study, under the paradigm of Healthcare4Q, we propose a framework called the Quantum AIdriven Heart Health Framework (QAIHHF) that can provide advanced predictive intelligence to healthcare providers by utilizing historical and real-time data and processing capabilities proposed in Healthcare4Q. We show that when applied to various diagnostics and health indicators such as ECG data, the Quantum AI provides accuracy at a level equal to or higher as compared to the classical methods thus proving itself to be the critical component that will herald the era of Healthcare4Q.
Psychiatric disorders represent some of the most biologically complex challenges in medicine, arising from intricate interactions among genetic, epigenetic, environmental, developmental, and social factors. Advances in artificial intelligence (AI) have improved our ability to analyze large-scale biological datasets, identify biomarkers, and support precision medicine initiatives. However, the growing volume and complexity of genomic and multi-omic information increasingly challenge the capabilities of even the most advanced conventional supercomputers. Quantum computing offers a potential next step in biomedical discovery by enabling rapid analysis of multidimensional datasets, molecular simulations, and optimization problems relevant to genetic medicine and gene therapy. Extending these findings conceptually, we propose the forward-looking hypothesis that continued advances in quantum computing may eventually complement artificial intelligence and human expertise to facilitate increasingly sophisticated analyses relevant to psychiatric genetics and precision medicine. At present, no direct evidence of which we are aware demonstrates clinical implementation of quantum computing in psychiatric genomics. Accordingly, the concepts discussed in this Opinion should be viewed as a forward-looking scientific perspective that builds upon current advances in computational science and biomedicine while awaiting future experimental and clinical validation. Importantly, these advances should complement rather than replace human expertise. Human-in-the-loop systems remain essential for ensuring scientific rigor, ethical oversight, clinical judgment, and patient-centered care. The convergence of quantum computing, AI, genetic medicine, and human expertise may ultimately establish a transformative framework for future precision psychiatry and mental health therapeutics.
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