Artificial intelligence is rapidly moving from the realm of theory and hype into practical, clinical use in oncology, offering new avenues to detect cancer earlier, personalise treatment and support doctors in managing increasingly complex cases. Rather than viewing cancer as a single disease or a mutation in one gene, researchers such as Debarka Sengupta, associate dean of Innovation, Research and Development at IIIT-Delhi, are using AI and genomics to study it as a complex biological system, mining vast molecular datasets to understand how tumours behave and respond to different therapies.
One of the most visible impacts of AI in cancer care today is in screening and diagnosis, where machine learning models are being trained to analyse radiology and pathology images with high sensitivity, flagging subtle tumour-like structures on MRIs, CT scans and mammograms that might otherwise be missed. These tools can help radiologists prioritise suspicious cases, reduce reporting time and support earlier intervention, which is critical for improving survival in cancers such as breast, lung and colorectal. AI is also being applied to genomic and transcriptomic data to identify personalised molecular signatures, enabling more precise risk stratification and opening pathways for targeted therapies and immunotherapy.
Beyond diagnosis, AI is increasingly being used to support treatment planning and optimisation. Studies have shown that AI can help predict treatment effects in tumour patients, personalise regimens based on individual disease characteristics and assist in adapting radiation doses and surgical approaches in real time. In radiation oncology, for example, AI-driven systems can help contour tumours more accurately and design adaptive treatment plans that respond to changes in patient anatomy over the course of therapy, potentially reducing toxicity and improving tumour control. At the same time, AI is being explored in clinical trials to streamline study design, patient recruitment and data analysis, which could accelerate the development of new cancer treatments.
However, experts caution that the shift from “fear to hope” around AI must be grounded in rigorous evidence and responsible deployment. Challenges remain around data privacy, algorithm bias, explainability and equitable access, particularly in resource-constrained settings where cancer burden is high. Researchers emphasise that AI should be viewed as a powerful support tool rather than a replacement for clinicians, with doctors retaining final responsibility for diagnosis and treatment decisions. As more AI-enabled tools are tested in well-designed clinical trials and integrated into everyday practice, the technology is expected to play an increasingly central role in India’s fight against cancer helping move the narrative from fear and uncertainty towards earlier detection, more personalised care and better outcomes for patients.
