Bridging diagnosis and therapeutics: The role of AI in cancer detection and drug development

Cancer remains a leading cause of global mortality, and its management is complicated by tumour heterogeneity, late detection, variable patient responses, data privacy, algorithmic bias, integrating clinical workflows, and the need for accurate patient selection and treatment monitoring, which are critical for improving therapeutic outcomes. Traditional diagnostic methods, including histopathological diagnosis using H&E-stained images, are often time-consuming and prone to variability. Artificial Intelligence (AI) has emerged as a transformative tool in oncology, capable of analysing heterogeneous data to identify complex patterns and provide accurate, non-invasive solutions. By leveraging multidimensional datasets, such as high-resolution histopathological slides, ML and DL models can be used for a wide range of image processing tasks, including classification, segmentation, and improved diagnostics throughout the cancer care continuum via sophisticated imaging analysis, digital pathology, and biomarker interpretation, facilitating accurate tumour diagnosis and patient classification. In addition, AI supports personalised treatment planning, predicts responses to immunotherapy, and facilitates drug discovery by identifying novel targets, predicting compound efficacy, and optimising molecular design, reducing both time and cost in anti-cancer drug development. The implications of AI in healthcare systems, along with associated challenges in data integration, algorithm interpretability, and ethical considerations, are emphasised, highlighting the potential of AI to revolutionise cancer care and improve patient health management. Ultimately, the convergence of human clinical expertise and augmented intelligence establishes a resilient, scalable infrastructure for oncology. This paradigm promises to enhance global survival rates by facilitating earlier detection, de-risking clinical development, and ensuring that therapeutic interventions are precisely tailored to each patient's unique biological profile.