Cancer is a heterogeneous and multifaceted disease with a significant global footprint. Despite substantial technological advancements for battling cancer, early diagnosis and selection of effective treatment remains a challenge. With the convenience of large-scale datasets including multiple levels of data, new bioinformatic tools are needed to transform this wealth of information into clinically useful decision-support tools. In this field, artificial intelligence (AI) technologies with their highly diverse applications are rapidly gaining ground. Machine learning methods, such as Bayesian networks, support vector machines, decision trees, random forests, gradient boosting, and K-nearest neighbors, including neural network models like deep learning, have proven valuable in predictive, prognostic, and diagnostic studies. Researchers have recently employed large language models to tackle new dimensions of problems. However, leveraging the opportunity to utilize AI in clinical settings will require surpassing significant obstacles—a major issue is the lack of use of the available reporting guidelines obstructing the reproducibility of published studies. In this review, we discuss the applications of AI methods and explore their benefits and limitations. We summarize the available guidelines for AI in healthcare and highlight the potential role and impact of AI models on future directions in cancer research.

An undergraduate student in physics at The University of Texas at Arlington earned a top award from the Texas Section of the American Physical Society (APS) for his research using positrons—the antiparticle of the electron—to study the electronic properties of graphene. Notable for being extraordinarily thin and strong, graphene is being researched for a wide range of applications, from solar cells and battery storage to cancer treatments and bone growth.

A collaboration of scientists and doctors from the Champalimaud Foundation has obtained exciting imaging results that could open the way to the early, non-invasive detection of pancreatic cancer.

Researchers have developed a new technique for head and neck reconstruction using a pedicled latissimus dorsi flap. This approach offers a safer, faster alternative for high-risk patients, achieving a 100% success rate in 22 cases with complex defects.

Huntsman Cancer Institute researchers have identified a gene variant found in a high-altitude Andean population that may predict severity of diseases and responses to treatments for patients with certain types of blood cancer.

Salk scientists have discovered that our specialized immune cells, called T cells, are what they eat—their switch from functional to "exhausted" depends on the switch from metabolizing acetate to metabolizing citrate. The findings link what T cells "eat" and whether those T cells can continue fighting cancer or chronic diseases like HIV. With the new insight, scientists can optimize immunotherapy for patients by targeting the nutrients and enzymes involved in making and maintaining active, disease-fighting immune cells.

Researchers have discovered a protein variant that serves as a knob for regulating the body’s innate immune response. The findings could lead to new therapies for Long COVID, autoimmune disorders, and more.