# Quantum Computing and Machine Learning in Medical Decision-Making: A Comprehensive Review
## 3 Applications in Medical Decision-Making
### ML Applications  
ML algorithms are increasingly used in medical imaging and pathology to assist in diagnosing diseases with higher accuracy and speed. In medical imaging, techniques such as CNNs are widely employed to analyze images from X-rays, CT scans, MRIs, and other modalities [39]. These algorithms can automatically detect and classify abnormalities, such as tumors or lesions, which helps radiologists and clinicians make quicker, more informed decisions [40]. By training on large datasets of annotated images, ML models can identify patterns that might be overlooked by the human eye, leading to earlier detection and improved outcomes for patients. In digital pathology, ML algorithms are applied to analyze histopathological slides of tissue samples, helping pathologists identify disease markers, cancerous cells, and other abnormalities [41]. These AI-powered diagnostic systems can assist pathologists by highlighting areas of concern and providing quantitative insights, such as tumor grading, which are essential for determining the severity of a disease and making treatment decisions. Moreover, ML can be used to detect rare conditions or atypical patterns in tissue samples, improving the sensitivity and specificity of diagnostic tests [42]. One of the most significant advantages of ML in healthcare is its ability to predict patient outcomes and track disease progression. By analyzing large datasets, such as patient records, laboratory results, and clinical data, ML models can identify risk factors and predict the likelihood of disease progression or recurrence [43]. These predictive models are invaluable in fields like oncology, where understanding the progression of cancer and the likelihood of metastasis is critical for developing effective treatment plans [44]. For example, in oncology, ML algorithms can predict how patients will respond to specific cancer treatments based on their medical history, genetic data, and tumor characteristics. By integrating clinical data with genomic information, ML can help identify the most effective therapies for individual patients, improving survival rates and minimizing the risk of unnecessary side effects [45]. Furthermore, predictive modeling can assist in the early detection of complications such as sepsis or organ failure, allowing clinicians to intervene early and prevent adverse outcomes [46]. ML has also become a key component in personalized medicine, where treatment plans are tailored to the individual characteristics of each patient [47]. AI-based decision support systems analyze patient data, including genetic, demographic, and clinical information, to recommend the most effective treatment strategies. These systems can provide clinicians with evidence-based treatment options that are personalized for each patient's unique needs, leading to more precise and effective interventions. In precision oncology, for example, ML algorithms can analyze genetic mutations and molecular profiles of tumors to recommend targeted therapies that are more likely to succeed [48]. By integrating data from clinical trials, real-world patient outcomes, and genetic databases, AI-based systems can continuously update and refine treatment recommendations, ensuring that patients receive the best possible care based on the latest research and their specific condition. Moreover, AI-driven decision support tools are increasingly used in other specialties, such as cardiology, neurology, and endocrinology, to help clinicians make data-driven decisions that optimize treatment outcomes [49; 50]. Figure 4 shows the iterative workflow of a RL approach to precision oncology. It begins with multimodal data from genetic assays, laboratory tests, radiographic images, and electronic health records, which serve as input for the RL framework. This framework, depicted as a deep neural network, selects treatment decisions based on its policy. These decisions impact tumor response and toxicity, ultimately influencing long-term patient outcomes.