# Quantum Computing and Machine Learning in Medical Decision-Making: A Comprehensive Review
## 2 Foundational Concepts
### Data Analytics in Healthcare  
The application of data analytics in healthcare has transformed the way medical professionals make clinical decisions, offering new insights and improving patient outcomes. Healthcare data are vast and diverse, encompassing a variety of types, each contributing to different aspects of patient care and medical research.  
Imaging data is one of the most critical types of healthcare data, particularly in diagnostics. Medical imaging techniques, such as X-rays, CT scans, MRIs, and ultrasounds, generate visual representations of the interior of a patient's body. These images are essential for diagnosing a wide range of conditions, from broken bones to tumors, and are often used in conjunction with other data sources to make more accurate assessments. Data analytics plays a crucial role in processing and analyzing imaging data, enabling tools like automated image segmentation and anomaly detection, which can enhance the speed and accuracy of diagnoses [28; 29]. For example, Figure 3 illustrates various ML algorithms, including classification, object detection, and segmentation, used to identify spleen injuries from CT images. These algorithms aim to determine whether the organ is injured, detect the target lesion, and perform voxel-level classification.  
Genomic data refer to information derived from an individual's genetic material. With the advent of genomic sequencing technologies, vast amounts of genetic data are now available, enabling researchers and clinicians to better understand the genetic basis of diseases and tailor treatments accordingly. Analyzing genomic data allows for the identification of genetic mutations or variations that influence disease development, drug responses, and the effectiveness of treatments. Data analytics techniques, such as ML, are used to analyze and interpret complex genomic datasets, allowing for the identification of biomarkers and more personalized approaches to treatment [30,31].  
Electronic Health Records (EHRs) are another critical data source in healthcare. EHRs contain detailed information about a patient's medical history, diagnoses, treatments, medications, allergies, lab results, and more. This data are stored digitally, enabling healthcare providers to access patient information quickly and efficiently. Analyzing EHRs through data analytics can identify trends in patient health, predict potential risks, and improve care coordination. EHR analysis also helps in clinical decision-making, providing healthcare professionals with insights that guide treatment plans and interventions [32].  
The importance of accurate and efficient data analysis in clinical decision-making cannot be overstated. With the increasing volume and complexity of healthcare data, clinicians face the challenge of processing and interpreting large datasets in a timely manner. Accurate data analysis is crucial for ensuring that clinical decisions are based on the best available information, whether that involves diagnosing a disease, selecting an appropriate treatment, or predicting patient outcomes. Effective data analysis can lead to better diagnosis accuracy, personalized treatment plans, and improved patient safety. However, for data analytics to be most effective, it must be coupled with high-quality data, robust analytical methods, and appropriate interpretation by healthcare professionals.