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Integration of quantum artificial intelligence in disease diagnosis: A review of methods and applications
Shobha Sharma1, Lokesh Sharma2, Tapan Kumar Gandhi1
1Department of Electrical Engineering, Indian Institute of Technology (IIT), Delhi, New Delhi, India.
Background And Objective:
Accurate disease diagnosis is vital for effective treatment and improved patient outcomes. While artificial intelligence (AI) has advanced medical diagnostics, conventional AI approaches often face limitations in real-time data processing, scalability, and managing high-dimensional biomedical data. Quantum Artificial Intelligence (QAI) integrates quantum computing with AI to address these challenges. This study explores QAI models in disease diagnosis, highlighting their advantages over classical AI, their applications across diseases, and integration possibilities within diagnostic workflows.
Methods:
A structured literature review was conducted using Scopus, PubMed, IEEE Xplore, and Google Scholar databases. A total of 37 peer-reviewed articles were selected based on relevance, methodological quality, and focus on QAI applications in diagnostics. The review analyzed key quantum machine learning (QML) models, including hybrid and quantum inspired techniques.
Results:
The findings indicate that QAI demonstrates promising applications in diagnosing cancer, neurodegenerative disorders, cardiovascular diseases, COVID-19, and other conditions. Quantum algorithms enable faster and more accurate pattern recognition in complex medical datasets. Additionally, QAI can be integrated into various stages of the diagnostic pipeline, from feature engineering to optimization to provide clinical decision support. However, technical challenges such as quantum noise, hardware instability, and limited algorithm maturity were frequently noted.
Conclusions:
QAI has the potential to revolutionize disease diagnosis by overcoming many limitations of classical AI systems. While significant progress has been made, real-world clinical integration requires further advancements in algorithm development and hardware scalability. Future research should focus on closing the gap between theoretical models and clinical implementation to fully realize the benefits of QAI in healthcare.
