Big Data

An artificial intelligence (AI) algorithm is capable of estimating a woman's risk of developing breast cancer in the next four years, according to a study published in The Lancet Digital Health. The tool identified women at high risk of developing breast cancer, and nearly one in ten of those who scored in the top 2% according to the algorithm were diagnosed within four years, despite having been discharged from hospital. The tool used mammograms from nearly 400,000 women and was then tested with data from nearly 96,000 women in Australia. The results were confirmed in a Swedish population of more than 4,500 women.

Over the last 30 years, critical evolutions in oncology have mirrored those in technology, and so it is with artificial intelligence (AI), which is increasingly transforming the oncology research landscape. Current agentic AI models are already capable of performing multiple different tasks with varying levels of precision and accuracy in a semi-autonomous manner.

Artificial intelligence (AI) tools can improve breast screening performance but different screening sites have varying needs. Here the GEMINI prospective evaluation of 10,889 women, within one UK region, used both live AI integration and simulations to model 17 different ways AI could be used in breast screening.

Patients diagnosed with breast cancer exhibit a diverse range of prognostic outcomes due to the varied nature of the disease across different patient groups. To address this complexity and enhance prognostic predictions based on gene expression data from breast cancer samples, this study has developed an integrated deep learning method that combines Convolutional Neural Networks (CNN) with Bidirectional Long Short-Term Memory (BiLSTM) networks.

Lung cancer remains a global health challenge that requires early and accurate diagnosis through medical imaging analysis. This study introduces ARXAF-Net framework which integrates Active Reinforcement deep leaning with strategic feature engineering, selection, advanced classification techniques with Explainable AI.

Predictive biomarkers to guide selection of first-line chemotherapy for advanced pancreatic ductal adenocarcinoma are an unmet clinical need. This study used the Computational Histology Artificial Intelligence platform to develop and validate a histomorphology-based G-chemo versus F-chemo biomarker that predicts benefit from first-line fluoropyrimidine-based versus gemcitabine-based regimens.

Imagine leaving a routine breast screening believing everything is fine, only to face an aggressive cancer diagnosis months later. A new generation of AI mammograms is quietly changing that story, cutting these missed cases and catching tumours when treatment is most effective.

Artificial intelligence (AI) has emerged as the “connective tissue” that will facilitate oncology practice and deliver enhanced care to patients, according to Arturo Loaiza-Bonilla, MD, MSEd, FACP.

Loaiza-Bonilla, systemwide chief of Hematology and Oncology at Saint Luke’s University Health Network, spoke with CancerNetwork® about his insights regarding recent advancements for AI in oncology and provided a recap of some of the key developments with these technologies in 2025.

Artificial intelligence is quickly becoming a revolutionary and game-changing tool in modern oncology, with promising uses in early diagnosis and drug discovery. Machine learning, deep learning, reinforcement learning, natural language processing, and generative models are some of the AI methods that are becoming very important for cancer care. With an emphasis on early diagnosis, mutation mapping, and drug design, this article aims to review the existing literature and investigate the role of AI technologies in oncology.

Liver cancer is a serious disease that can be difficult to detect early. When a patient’s symptoms raise a concern, physicians usually use medical images, such as CT scans or MRIs, to look for signs of cancer or abnormal growth in the liver. But sometimes, these images are challenging to read, and small tumors can be missed. That’s where artificial intelligence (AI) comes in. A recent study looked at how AI is being used to help doctors find and understand liver cancer better.

Artificial intelligence (AI) models are increasingly applied in clinical oncology, yet their comparative utility in specialized domains like bone and soft tissue tumors remains understudied. This study evaluates the diagnostic accuracy and clinical reasoning capabilities of DeepSeek and ChatGPT.

A recent study published in the Journal of Translational Medicine examines the role of artificial intelligence (AI) in advancing precision oncology. The review, conducted by researchers R. Goda and A. Abdel-Aziz, highlights significant developments in how AI technologies are being applied to cancer treatment strategies. The authors analyze a range of applications, including AI’s ability to enhance diagnostic accuracy, predict patient outcomes, and personalize treatment plans based on individual genetic profiles.