Big Data

Head and neck cancers present significant challenges in diagnosis, treatment planning, and prognostication due to their heterogeneous nature and anatomical complexity. Artificial intelligence, particularly convolutional neural networks, has emerged as a transformative tool for developing clinical decision support systems that enhance precision in oncology. However, the breadth of AI applications in HNC has often obscured specific insights into their clinical impact. This umbrella review critically evaluates the role of AI-supported CDSS, with a focus on CNN-based models, in improving diagnostic accuracy, guiding treatment decisions, and refining prognostic predictions in HNC.

The extensive heterogeneity of cancer across biological scales necessitates a holistic approach beyond single-analyte methods. Integrating multi-omics data — from genomics to proteomics — with multimodal information, such as clinical records and medical imaging, offers a comprehensive, systems-level view of tumorigenesis. Artificial intelligence (AI) has emerged as the essential technology to decipher these complex, high-dimensional datasets, powering substantial advances in early diagnosis, precise patient stratification, prediction of therapeutic response and the elucidation of mechanisms of drug resistance. To translate these powerful predictive models into practice, explainable AI is critical for building clinical trust and generating novel, testable biological hypotheses. While challenges in data accessibility and model generalizability persist, the field is advancing toward patient-specific digital twins, promising to simulate individual disease trajectories and optimize treatments, thereby heralding a new era of precision oncology.

Drug discovery has been constrained by extended timelines and high costs, as the cumulative requirements of preclinical validation, multi-phase clinical trials, and regulatory approval have been imposed. Recently, computational modeling has been explored as a supportive approach to accelerate the identification and refinement of therapeutic candidates.

Whole-slide imaging is now a validated component of modern pathology practice, supported by increasingly robust foundation models that generalize across stains and scanners and by updated Clinical Laboratory Improvement Amendments (CLIA) guidance that clarifies digital and remote workflows. Just as importantly, the same biopsy can now support triage, prognosis, molecular testing, and even treatment selection without additional tissue handling. This convergence of validated digital slides, stronger AI backbones, and clearer regulatory footing sets the stage for the next shift: AI that doesn’t just detect cancer but informs treatment decisions.

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.