# Quantum Computing and Machine Learning in Medical Decision-Making: A Comprehensive Review
## 1 Introduction  
Medical decision-making is a cornerstone of modern healthcare, encompassing processes that guide diagnosis, treatment planning, and patient management. Accurate and timely decisions are critical to improving patient outcomes, optimizing resource allocation, and enhancing the overall efficiency of healthcare systems [1]. However, the increasing complexity of medical data, driven by advancements in imaging, genomics, and electronic health records, presents significant challenges for clinicians. This data-rich environment necessitates innovative computational tools to extract actionable insights and support clinical decision-making [2]. Advanced computational techniques have emerged as indispensable in addressing these challenges. Among them, machine learning (ML) has demonstrated remarkable capabilities in processing and analyzing large volumes of heterogeneous medical data [3, 4]. ML algorithms excel in tasks such as pattern recognition, predictive modeling, and anomaly detection, making them invaluable in fields like medical imaging, drug discovery, and personalized medicine [5]. However, as datasets grow in size and complexity, classical computational approaches often struggle to deliver the speed and efficiency required for real-time decision-making. Quantum computing (QC) leverages the principles of quantum mechanics and holds the potential to outperform classical systems in certain optimization problems relevant to medical applications. However, their current implementations remain limited, and theyhave yet to demonstrate a practical advantage over classical methods in processing large, complex datasets for real-world machine learning tasks [6]. While quantum speedup has been observed in specific, carefully designed problems, its applicability to broader medical computations is still an area of active research. Figure 1 shows the diverse applications of QC in medicine, showcasing its potential to transform areas such as drug design, genomics, medical diagnostics, AI-enhanced healthcare, and radiotherapy. QC's enhanced computational power and efficiency are poised to significantly advance these fields, improving both precision and speed in medical research and clinical practice. Each branch represents a key area where QC can make substantial contributions. When integrated with ML, QC holds the potential to transform medical decision-making, enabling unprecedented precision and speed in analyzing complex medical datasets [8]. Applications range from optimizing treatment protocols to predicting disease progression and personalizing therapies. Figure 2 presents a comparison of quantum computing paradigms and classical computing approaches, highlighting their respective strengths, limitations, and potential applications. While the figure is reproduced from a previous review [6], its claims have been critically assessed. Notably, the term \"suitability for routine and complex processing\" requires further QC work to support it, as many advanced computational challenges, such as protein folding, have been successfully addressed using classical computing. Unlike conventional comp  
Figure 1: Schematic diagram illustrating the key applications of quantum computing (QC) in medicine. These include drug design and molecular simulation, genomics and personalized medicine, medical diagnostics, AI-enhanced healthcare, and Monte Carlo simulations in radiotherapy. QC’s advanced computational capabilities have the potential to significantly improve accuracy, speed, and efficiency in these critical healthcare areas. Reproduced from reference [7] under the Creative Commons Attribution 4.0 International License ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)) (accessed on 24 January 2025)). tum computers operate with quantum bits, or \"qubits\", which can represent both '1' and '0' simultaneously.