Big Data

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.

Gastric cancer (GC) remains a global clinical challenge due to late diagnosis, high heterogeneity, and poor prognosis. Tumor stemness has emerged as a key factor driving tumor aggressiveness and therapeutic resistance. However, the systematic characterization of high-stemness GC cells and their molecular features remains limited. We integrated single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and bulk RNA-seq data to identify and characterize high-stemness GC cells.

While targeted radiation can be an effective treatment for brain tumours, subsequent potential necrosis of the treated areas can be hard to distinguish from the tumours on a standard MRI. A new study led by a York University professor in the Lassonde School of Engineering found that a novel AI-based method is better able to distinguish between the two types of lesions on advanced MRI than the human eye alone, a discovery that could help clinicians more accurately identify and treat the issues.

Cervical cancer prognosis critically depends on tumor invasiveness, yet existing predictive tools lack accessibility and generalizability. We aimed to develop predictive models using comprehensive hematological profiling of routine tests to assess invasiveness and survival, improving clinical decision-making.

Artificial intelligence (AI) is increasingly being integrated across the cancer care continuum, from early detection and diagnosis to treatment selection and clinical trial design. Standardized metrics are needed to ensure adequate, reliable machine learning model performance and clinical relevance across diverse settings. Becoming fluent in the language of AI will empower oncologists to participate in shaping the role of new technologies in oncology research and practice.

Artificial intelligence (AI) is opening new frontiers in the development of antibody-drug conjugates (ADCs), offering unprecedented opportunities for precision therapy. This review outlines how AI empowers each stage of the ADC pipeline.

Survival assessment for oral squamous cell carcinoma (OSCC) remains a significant clinical challenge. This study develops novel artificial intelligence (AI) platforms for assessing overall survival in OSCC patients based on 240 whole-slide images from multicenter cohorts.

Artificial intelligence (AI) is having a moment. As a field, AI dates almost as far back as medical oncology, to the dawn of the computer age in the mid-1950s. For decades, theory far outpaced application, and small advances in the clinical domain were followed by periods of disillusionment—the AI winters of the 1970s and 1980s-2000s.

Artificial intelligence (AI) continues to lead to innovative treatment plans and discoveries in the world of cancer treatment. For example, integrations between AI and non-small cell lung cancer (NSCLC) and ovarian cancer have been made over the last few years, and some physicians are utilizing AI for administrative tasks as well, such as transcribing subjective, objective, assessment, plan (SOAP) notes during patient visits.