The proposed fellowship uses multimodal cancer data to help predict the treatment response, personalise patient care, and improve survival and quality-of-life post-treatment. By harnessing diverse data sets, the AI models are trained to identify subtle patterns and indicators. This not only aims to customize treatment plans to individual patient needs but also seeks to mitigate the risks associated with radiotherapy, potentially reducing the occurrence of debilitating side effects and improving overall treatment efficacy. The project will focus on clinical, anatomical, and biological patient data. First, the project focuses on employing AI to analyze complex pathology slide images. This research is set to transform tumor diagnostics by providing unprecedented insights into the microenvironment of cancers. This approach aims to uncover new diagnostic markers and patterns, enhancing the accuracy of tumor classification and staging. The fusion of these AI-generated insights with the recognised prognostic feature of pathologists will lead to more precise results. Furthermore, recognising the gap in the application of AI in clinical settings, a part of the research is focused on developing a web-based platform that makes AI tools readily available and user-friendly for clinicians. The platform is envisioned to provide non-specialist healthcare professionals with access to state-of-the-art AI analysis for radiology and histopathology. This means that clinicians can benefit from AI-powered insights in real-time, enhancing their decision-making process in patient care. This service aims to democratize the use of AI in healthcare, making it a standard part of clinical practice and thus accelerating the adoption of AI in medical diagnostics and treatment planning. Collectively, these components of my research will represent a significant leap forward in the application of AI in the realm of cancer care. The project is not just about technological innovation; it's about fundamentally transforming the approach to cancer diagnosis and treatment.