Quantum AI's health revolution: Physics, progress, power, and the person

Dominique J Monlezun

Ruchi Mangharamani, Gaurav Raj

Quantum-Driven Predictive Healthcare represents a pioneering integration of quantum computing principles with advanced risk modeling and artificial intelligence to revolutionize medical decision support systems. This approach harnesses quantum algorithms to process vast, complex healthcare datasets, enabling rapid identification of subtle patterns and correlations that traditional computing systems may overlook. By incorporating quantum-enhanced risk modeling, clinicians can benefit from more precise predictions of disease progression and patient outcomes, ultimately facilitating early intervention and personalized treatment strategies. AI-powered decision support further refines these predictions by dynamically adapting to new data inputs, learning continuously from real-time clinical feedback. The synergy between quantum computing and AI not only accelerates data analysis but also improves diagnostic accuracy and resource allocation within healthcare institutions. Moreover, the methodology promotes a deeper understanding of multifactorial health conditions by integrating diverse datasets—from genomics to electronic health records—into a coherent analytical framework. This fusion of technologies is poised to overcome the limitations of conventional statistical models, offering a robust, scalable, and future-proof solution for predictive healthcare. As healthcare challenges become increasingly complex, embracing quantum-driven solutions will be instrumental in transforming medical risk assessment and decision-making processes. The research presented herein lays the groundwork for next-generation clinical tools that merge computational innovation with practical healthcare applications, promising enhanced patient outcomes and more efficient medical care delivery.

Dominique J Monlezun

Hossein Hassani, Leila Marvian Mashhad, Steve MacFeely

Sakshi Gupta, Archana Singh, Neetu Singh

Traditional agriculture has been transformed with AI and ML models, and many problems have been solved to a great extent. However, many complex and advanced problems have not been solved yet. To solve these problems, the integration of quantum physics and quantum computing can be explored. This chapter gives insights into how quantum computing and its algorithms can provide solutions to those complex problems that are beyond the capacity of classical algorithms. This chapter highlights the revolution in areas such as crop health management, precision farming, crop disease management, pest prediction, livestock management, soil health management, and supply chain management.