# Quantum Computing and Machine Learning in Medical Decision-Making: A Comprehensive Review
## 3 Applications in Medical Decision-Making
### Synergy of QC and ML  
The integration of QC with ML is a rapidly evolving field that holds immense promise for advancing medical decision-making. By combining the computational power of quantum algorithms with the data-driven capabilities of ML, this synergy has the potential to transform healthcare, particularly in areas requiring large-scale data analysis, complex optimization, and advanced predictive modeling. Below, we explore how QC enhances traditional ML, the role of hybrid quantum-classical models, and some potential use cases in healthcare decision-making. Quantum ML (QML) refers to the application of QC to enhance traditional ML algorithms [51]. QC offers advantages in processing large datasets and solving computational problems more efficiently than classical computers. By leveraging quantum properties such as superposition and entanglement, QML can potentially perform certain ML tasks exponentially faster than classical methods. For instance, quantum algorithms can speed up matrix operations, a key component of many ML techniques, including principal component analysis (PCA) and clustering [52]. Quantum versions of popular ML algorithms, such as support vector machines (SVMs) [53] and neural networks [54], show promise for improving the efficiency of training models, particularly when dealing with high-dimensional data. In healthcare, where datasets are large and complex, QML could significantly accelerate tasks such as drug discovery, genomics, and medical imaging analysis [55; 56]. Furthermore, quantum algorithms improve the optimization processes in ML, enabling more accurate and timely predictions that could improve patient care [57]. While QC holds significant potential, current quantum hardware is still in its nascent stage, with limitations in qubit coherence times and error rates. As a result, researchers are exploring hybrid quantum-classical models, where quantum algorithms are integrated with classical ML methods to leverage the strengths of both approaches [58]. In these models, QC is used for specific tasks that benefit from quantum speedup (such as optimization and linear algebra operations), while classical computing handles tasks that are better suited to classical methods. In medical decision-making, hybrid quantum-classical models are particularly promising for complex optimization problems like treatment planning, personalized medicine, and drug discovery. For example, quantum computers could optimize the allocation of radiation doses in cancer treatment planning, considering various patient-specific factors, while classical systems could handle patient data analysis and clinical decision support [59]. By using quantum-classical hybrids, healthcare providers can harness the advantages of both quantum speed and classical reliability to improve treatment accuracy and efficiency. There are several promising use cases for the synergy between QC and ML in healthcare decision-making. For example, QML can accelerate drug discovery by simulating complex molecular interactions more efficiently than classical computers [60]. By combining quantum simulations with ML algorithms, researchers can more quickly identify promising drug candidates and predict their interactions with biological targets. This approach could significantly shorten the time required for drug development, reducing costs and improving the availability of new treatments. Moreover, QML can be used to develop more accurate predictive models for personalized medicine [61]. By analyzing vast datasets, including genomic data, clinical records, and medical imaging, quantum-classical hybrid models could help predict patient responses to specific treatments. For example, in oncology, QML could improve the accuracy of predicting cancer recurrence or response to chemotherapy, enabling more tailored treatment plans that optimize patient outcomes [62]. In addition, QML has the potential to transform medical imaging by improving the speed and accuracy of image analysis [63].