Citation

Shirin, Najnin Sultana and Hasan, Md Mehedi and Morol, Md Kishor and Fahad, Nafiz and Hosain, Md Tanzib and Hossen, Md Jakir and Nandi, Dip (2026) Quantum computing in medical diagnostics and treatment: A systematic review of trends, challenges and future directions. Intelligence-Based Medicine, 13. p. 100356. ISSN 26665212

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Abstract

The growing digitalization of medical diagnostics has resulted in massive amounts of complicated data, including medical imaging and genomic sequences, clinical writing and real-time patient monitoring. Classical machine learning (CML) has achieved amazing success in evaluating such data. But its computational restrictions hinder scalability and efficiency when dealing with high-dimensional biomedical problems. Quantum machine learning (QML) combines the principles of quantum computing (QC) with advanced learning algorithms to offer a transformative paradigm for digital healthcare. This paper provides a systematic overview of QML foundations including quantum data encoding (QDC), variational quantum circuits (VQC), kernel methods, and hybrid quantum classical models. This paper also focuses on their applications in medical imaging, genomics, natural language processing (NLP) for electronic health records, drug discovery and healthcare security. We present comparative insights between classical and quantum approaches such as slow processing of high-dimensional data, limited scalability and inefficiency in complex optimization problems. This review also emphasizes the emerging directions towards quantum-based personalized digital healthcare approaches. By combining medical science with quantum QML has the potential to revolutionize the future of precision diagnostics, treatment optimization and healthcare data security. This study also provides a valuable resource for those interested in quantum computing and researchers who want to stay updated on the fast-growing area.

Item Type:	Article
Uncontrolled Keywords:	Machine learning, deep learning
Subjects:	R Medicine > R Medicine (General) > R858-859.7 Computer applications to medicine. Medical informatics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	02 Apr 2026 08:10
Last Modified:	02 Apr 2026 08:10
URII:	http://shdl.mmu.edu.my/id/eprint/15677

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