## 3 Results
### Artificial Intelligence--Using Data to Improve Cancer Care
Breast cancer screening uses mammography, prostate cancer screening uses MRI, and lung cancer screening uses low-dose CT scans. Early detection is key for patient outcomes and survival; hence, these imaging modalities are essential. Despite its broad use, AI in cancer screening is still in its infancy. AI has shown great promise in improving diagnosis accuracy, particularly in mammography, but its full potential in population-wide screening has yet to be achieved. Many studies have used AI to read mammograms to detect breast cancer early by spotting patterns that radiologists may miss. ML techniques in these AI systems can analyse mammographic pictures to detect microcalcifications and masses faster and more accurately than previous methods [37; 38; 39]. [PERSON] et al. 's population-based-screening study showed that AI can detect breast cancer through mammography. The study found affordable AI systems could improve screening approaches, especially in areas without professional radiologists. These AI-driven technologies can swiftly evaluate vast amounts of images to help radiologists make more accurate diagnoses. AI in the screening process may reduce healthcare workers' burden, reduce human error, and detect more breast cancer cases at an earlier, more treatable stage [38]. Natural Language Processing (NLP) is another promising AI cancer treatment technology. NLP is an area of artificial intelligence that helps computers understand, interpret, and generate meaningful and usable human language. NLP can evaluate massive amounts of unstructured data in medical notes, EHRs, and patient feedback to improve clinical decision-making in cancer care. NLP is used in cancer to find patterns and trends in complex medical records that healthcare providers may miss. NLP algorithms can analyse narrative medical notes to extract patient history, symptoms, and treatment reactions to forecast the best treatments. This is especially useful when patients have extensive medical histories or several therapy routes, and picking the best one is crucial to their results [40]. A study conducted by [PERSON] et al. employed an NLP model to evaluate data from 2500 patients diagnosed with non-small cell lung cancer (NSCLC), which is the predominant form of lung cancer, representing around 85% of all lung cancer cases. A complete dataset of unstructured clinical notes was utilised in the study, encompassing complete recordings of patient contacts, oncologists' observations, and documented events, such as symptom progression, treatment responses, and adverse effects. The NLP model examined these narrative data points to detect patterns and connections that may not be readily evident through conventional data analysis approaches. Significantly, the model accurately forecasted mortality in these patients by analysing the observed improvements and deteriorating events as reported by oncologists over a period of time. The shown predictive capacity of NLP underscores its potential to improve clinical decision-making in the oncology field by offering early indications of patients who are more likely to have unfavourable outcomes [41]. Integrating ML into the current digital cancer care systems and procedures will be fairly difficult. To optimise these models' effectiveness and impact, ensuring they can be easily incorporated into decision support systems, clinical processes, and other healthcare services is crucial. Cooperation between data/ML engineers, medical professionals, patients, family caregivers, and other participants is needed to construct and carry out efficient solutions considering the particular requirements and limitations of cancer treatment.