## 3 Results
### Artificial Intelligence--Using Data to Improve Cancer Care  
As digital technology has been developing rapidly, a transition to the digital storage of data has been made. Moreover, with time, storage spaces expanded and became more complex, and advances in processing power made it feasible for artificial intelligence to be implemented in medical care. AI is a revolutionary technology that aims to mimic, improve, or transcend human intellect by creating computers that can perform tasks that need human thought. Automation that can adapt to new circumstances, discover complicated patterns, and draw general inferences from large datasets can improve learning, reasoning, problem-solving, and decision-making. AI's ability to make accurate interpretations, alone or with human help, is being used in numerous fields, including healthcare, where it could revolutionise diagnosis, treatment, and patient care. Machine learning (ML) is a fundamental topic of advanced AI that creates algorithms that learn from data. Algorithm accuracy and prediction power increase with data quantity and quality. ML models improve their outputs by finding subtle patterns and making exact predictions when they are exposed to additional datasets. ML is crucial in cancer, where large datasets from medical imaging, genetics, and patient records can be used to predict disease progression or modify treatments. Deep learning (DL) is a more advanced area of AI that uses neural networks to mimic the human brain. These networks use layers of interconnected \"neurons\", or nodes, to examine raw data and gain insights. DL can process massive amounts of unstructured data, including photos, text, and audio, without feature extraction, making it powerful. DL models can autonomously recognise patterns and correlations in data that humans may miss, making them useful in medical imaging to detect minor irregularities that may indicate early cancer or other illnesses. In digital health platforms, AI, ML, and DL can improve clinical decision-making, workflows, and patient outcomes by providing more accurate, efficient, and personalised healthcare solutions [33]. AI, especially medical image processing, has transformed cancer diagnosis. This field of AI extracts clinically useful information from medical images to improve diagnosis and therapy. Medical image computing analyses complicated visual data using numerous methods to detect and track cancer. Image segmentation, which divides the image into organ and tissue segments, is a common method. Isolating malignancies from healthy tissue is crucial for accurate diagnosis and tailored treatment planning [34]. To simplify understanding, picture registration aligns several images from various times or modalities. This is crucial for following cancer progression or comparing CT and MRI scans to obtain a complete picture of the patient's status. Clinicians can detect subtle tumour size, shape, and location changes by correctly aligning these images, resulting in faster and more accurate diagnosis. AI also improves picture acquisition, making it more efficient and personalised for cancer patients. For instance, AI systems can balance CT scan doses to maximise picture quality and minimise radiation exposure. This feature is useful for cancer recurrence monitoring patients needing frequent imaging. AI can reduce MRI scan times, which can be uncomfortable for patients. AI optimises scan processes to speed up and simplify imaging while maintaining excellent quality [35]. Convolutional neural networks (CNNs) are also changing MRI contrast agent management. For nephrotoxic chemotherapy patients, contrast chemicals must be carefully managed to avoid kidney injury. CNNs can process raw imaging data in real-time and precisely change contrast levels to provide diagnostic information without sacrificing patient safety. This enhances imaging quality and minimises contrast agent hazards, making cancer diagnosis safer and more efficient for susceptible patients [36].