A Multidisciplinary Quantum-Based Clinical Decision Support System to Advance Medical Diagnostics and Treatment

Complex diseases like neurological disorders, rare pediatric conditions, chronic kidney diseases, sepsis, and complications from maxillofacial surgery are putting pressure on the global healthcare system. As the volume of medical data grows, ranging from imaging and physiological signals to electronic health records, there is an increasing need for more precise and personalized approaches to diagnosis and treatment. Traditional methods often fall short in handling this complexity, but quantum computing offers a promising solution. With its ability to perform large-scale, parallel computations using principles like superposition and entanglement, it opens new possibilities for tackling these medical challenges.

This project aims to revolutionize clinical decision support systems by leveraging advanced quantum techniques. We will integrate quantum machine learning (QML) methods, including quantum decision fusion, fuzzy clustering, reinforcement learning, and Siamese neural networks, to improve diagnostic accuracy, optimize treatments, and ultimately enhance patient outcomes. Our focus spans four critical medical areas: pediatrics, nephrology, neurology-sepsis, and maxillofacial surgery.

By addressing these pressing clinical challenges, our work not only pushes the limits of medical AI but also brings quantum technologies closer to real-world healthcare applications.

Faculty Supervisor:

Moulay Akhloufi

Student:

Partner:

Université de Sfax

Discipline:

Computer science

Sector:

Quantum Science; Artificial Intelligence; Health and Related Sciences and Technology

University:

Université de Moncton

Program: