58 Artificial intelligence (AI) is significantly transforming the landscape of cancer research, offering unprecedented capabilities in data analysis, drug discovery, and personalised medicine. The integration of AI into oncology promises to accelerate research productivity, enhance diagnostic accuracy, and improve patient outcomes through advanced data processing techniques. The findings were published in the journal Nature Reviews Cancer. One of the primary challenges in cancer research has been the effective utilisation of real-world data (RWD), which includes electronic health records (EHRs), medical images, tumour samples, and blood tests. Unlike structured clinical trial data, RWD is often unstructured and scattered across different systems, making it difficult to analyse comprehensively. AI-based tools are now being developed to tackle this issue, offering the potential to streamline data collection and analysis processes, thereby facilitating more robust and accurate research outcomes. Deep learning, a subset of AI, has been particularly impactful in cancer research. It excels in processing unstructured data such as medical images and genomic sequences. The capability of deep learning to identify patterns in large datasets allows for more precise tumour detection in imaging and a better understanding of genetic mutations associated with cancer. Researchers are increasingly using deep learning models to analyse MRI and CT scans, achieving accuracy comparable to that of experienced radiologists. AI is revolutionising drug discovery by expediting the identification and testing of new compounds. Deep learning models can screen potential drugs more efficiently by analysing both computational and experimental data. For example, AI algorithms can predict how different molecules will interact with target proteins, significantly speeding up the process of finding viable drug candidates. This approach not only saves time but also reduces costs associated with traditional drug discovery methods. The integration of AI in cancer research is paving the way for personalised medicine. By analysing a patient’s genetic information, AI can help develop tailored treatment plans that are more effective and have fewer side effects. AI algorithms can predict how a patient will respond to a particular treatment based on their genetic makeup and medical history, thereby enabling more precise and targeted therapies. Despite the promising advancements, the application of AI in cancer research is not without challenges. The complexity of AI systems and the need for large, high-quality datasets pose significant hurdles. Additionally, there are ethical considerations regarding data privacy and the potential biases in AI algorithms. Ensuring that AI systems are transparent and their decisions are explainable is crucial for gaining the trust of both healthcare professionals and patients. The future of AI in cancer research looks promising. As AI technologies continue to evolve, they are expected to become even more integral to cancer research and treatment. Researchers are optimistic that AI will lead to new scientific discoveries, improved diagnostic tools, and more effective treatments for cancer patients. The ongoing collaboration between AI experts and cancer researchers will be essential in overcoming current challenges and unlocking the full potential of AI in oncology.