AI and Cancer

How cells are arranged within tissue can reveal as much about disease as the cells themselves. When doctors examine a cancer tumor, for example, they don't just look at which cells are present; they also want to know how those cells are arranged and how they interact with each other. That's because the way cancer cells interact with neighboring immune cells can offer important clues about how a disease develops or responds to treatment.

AI and Early Pancreatic Cancer Every scan in the study had already been read and cleared as normal.

TUESDAY, Oct. 27, 2026 — Is that brown spot a sign of aging, or is it skin cancer?A new AI tool promises to help doctors suss out skin cancers, researchers reported Oct. 22 in the journal Mayo Clinic Proceedings.The tool, called Dermoscopy...

In a new paper published in Cell Reports Medicine, the UCL team outline how they, in collaboration with a University of Cambridge team, developed a new type of treatment-predicting computer program that uses an individual's detailed genetic fingerprint to explore which treatments, if any, may kill their cancer – especially when standard treatments have failed.

A powerful new AI system could help doctors detect pancreatic cancer years before it becomes visible on conventional scans. The technology, called REDMOD, identified hidden warning signs an average of 475 days before diagnosis and was nearly twice as sensitive as experienced radiologists. By spotting cancer-related changes at their earliest stages, it could dramatically improve the chances of successful treatment for one of the deadliest forms of cancer.

Could a selfie reveal clues about a cancer patient’s survival risk? Researchers have developed an AI model that estimates biological age and predicts mortality risk, using facial photographs of a patient.

While AI offers the ability to improve human experts' ability to diagnose and treat diseases like breast cancer, well-publicized errors across the broader AI field are cause for concern.

Mass General Brigham researchers say an AI tool can estimate cancer survival risk from a face photograph, but independent experts say it is not ready for clinical use.

Researchers are using AI tools to analyze simple facial photographs to determine key indicators of a person's health. Mass General Brigham researchers built FAHR-Face (Foundation Artificial Intelligence for Health Recognition), a large AI foundation model trained on over 40 million face images.

Researchers at Mass General Brigham developed experimental AI tools that use facial photographs to estimate cancer patients' biological age and mortality risk, though clinical trials are still needed.

Researchers tasked an algorithm with predicting cancer patients’ risk of dying. For some people, it was shockingly accurate

Palantir CTO Shyam Sankar says some AI researchers smoke in faith that AI will cure cancer, but offers no evidence as he warns against hype replacing medical proof.

Women who stay on aromatase inhibitors for 5 years have higher risks of hypertension, dyslipidemia, and diabetes vs their non-adherent counterparts. Yet their mortality rate is lower.

A secondary analysis of the RACC trial assesses perioperative safety and early quality of life after robot-assisted vs open radical hysterectomy in patients with early-stage cervical cancer.

Skin cancer is the most common cancer in the United States and among the most common worldwide. Nearly 1.5 million new cases were diagnosed globally in 2024, including nearly 340,000 melanomas.

Finding a few tumor cells hidden among billions of blood cells is one of the biggest challenges in liquid biopsy.

Researchers with Sylvester Comprehensive Cancer Center, part of the University of Miami Miller School of Medicine, have created a framework for developing, evaluating and implementing AI tools in cancer survivorship and supportive care, according to a new proof-of-concept study published in the journal Translational Behavioral Medicine.

Researchers have developed a powerful AI tool that reveals previously invisible patterns inside breast cancers. It uncovers how

Breast cancer screening must balance diagnostic accuracy with workflow throughput. DM remains the traditional cornerstone, but its two-dimensional projection creates tissue-overlap noise, particularly in dense breasts, leading to false positives and obscured lesions.

Artificial intelligence (AI) tools can help physicians predict treatment and survival outcomes in patients with advanced non-small cell lung cancer (NSCLC) treated with immunotherapy, according to a new study published in Nature Medicine.

Digital twins allow doctors to simulate treatment to see what will work best — and the tech has made real strides in cardiology. Oncology remains the bigger challenge.

A ChemSec report warns that leading PFAS manufacturers are planning production expansions driven by AI datacentres, semiconductors and batteries, despite growing regulatory efforts to limit exposure.

An AI algorithm accurately evaluates pathologic response after neoadjuvant chemoimmunotherapy in patients with NSCLC, showing moderate concordance with expert pathologist consensus across cohorts.

Researchers trained AI on how expert pathologists look for signs of cancer in patient samples, improving the algorithm's effectiveness.

Researchers compared five large language models on breast cancer health information using 90 knowledge questions, 10 clinical cases, and 20 common patient concerns, with all prompts submitted in simplified Chinese. The models performed strongly on standardized knowledge overall, but differed in linguistic complexity and expert-rated completeness, readability, helpfulness, correctness, and safety.

An artificial intelligence model that analyzes women's past and recent annual 3D mammograms is more effective at predicting five-year risk of developing breast cancer than a tool that uses only the most recent, single 3D mammogram, as well as an AI model that analyzes 2D mammograms, a new study shows.

Cornell researchers have used artificial intelligence to uncover a previously unknown way cells control how proteins move inside them, a process essential for growth, communication and movement that is often disrupted in cancer.

Without brain surgery guided by live AI assistance, Rhys Hibbert could have lost his sight to a tumour.

When rescue dog Chloe is not tumbling across the grass on a farm outside India's tech capital, Bengaluru, she is learning to sniff human breath for cancer with a little help from AI.

AI-assisted coherence tomography shows feasibility as a method of detecting basal cell carcinomas when they are still undetectable, but clinical relevance is still being evaluated.

Spatial transcriptomics is a powerful tool to create a detailed map of not just how genes are expressed in a piece of tissue, but also where genetic activity takes place in different cells and locations within the tissue.

Surgery remains the primary curative treatment for gastric cancer, with laparoscopic gastrectomy (LG) established as a standard minimally invasive approach.

AI is increasingly being used across precision oncology to accelerate drug discovery, predict resistance, and guide the design of nanoparticle-based drug-delivery systems. The review highlights AI-guided nanocarriers, adaptive treatment strategies, and multi-omics analysis while emphasizing challenges involving data quality, transparency, generalizability, and regulation.

Cancer diagnoses directly affect treatment decisions and prognosis and can even have life-altering consequences.

Anthropic CEO Dario Amodei envisions AI speeding up medical research, potentially achieving decades of progress in years and transforming cancer and Alzheimer's research, though challenges remain.

A research team at The Hong Kong Polytechnic University (PolyU) has developed a patient-centric "Artificial Intelligence (AI) Virtual Patient Simulation System", overcoming the limitations of conventional static diagnosis.

Indian startup Dognosis combines trained detection dogs with sensors and AI to flag cancer-associated signals from breath, with a 10,000-person Phase 3 trial underway.

Researchers have developed a novel AI model that can analyze a routine whole histopathology image and simultaneously predict cancer subtype, specific genetic mutations, and survival outcomes across 32 different solid cancers rather than focusing on a single cancer type.

A cancerous tumor is not a single enemy. It is made up of many different cell populations that can behave in very different ways.

Leica Biosystems introduces AI-driven quality control in digital pathology, streamlining workflows and enhancing diagnostic accuracy for better patient care.

A deep learning system trained on whole-slide images classifies nine renal tumor subtypes with high accuracy and outperforms traditional grading in predicting patient outcomes.

Insilico Medicine ("Insilico"), a clinical-stage generative artificial intelligence (AI)-driven biotechnology company, today announced the publication of a collaborative study in the journal npj Precision Oncology.

Researchers at the Johns Hopkins Kimmel Cancer Center validated an artificial intelligence (AI)-powered blood test that accurately detected liver cancer in people from two geographically and biologically distinct populations and also uncovered the underlying biological signals that make the test effective.

Researchers have developed a new artificial intelligence-based method that could significantly improve the detection and classification of brain tumors from MRI scans, achieving higher accuracy than conventional diagnostic systems.

Researchers developed Oncoformer, a multimodal transformer that analyzes longitudinal health records and chest X-rays to classify cancer, predict diagnoses up to one year in advance, infer tumor stage, and forecast treatment response and recurrence risk. Validation across large independent cohorts showed strong performance, although prospective trials in diverse, average-risk populations are needed before clinical use.

A new artificial intelligence (AI) tool developed by La Trobe University researchers could help predict which stage-two bowel cancer patients are at risk of relapse, in an advance that could ultimately lead to thousands getting life-saving treatment earlier.

Artificial intelligence developed by researchers at UCL has revealed a promising combination of cancer treatments that could help patients with locally advanced rectal cancer live longer.

From a routine pathology slide, CANVAS can predict immunotherapy resistance better than current clinical methods, offering a potential

The future of federally funded research at Harvard Medical School — supported by taxpayers and done in service

Researchers presented a groundbreaking artificial intelligence (AI)-guided technique today at the Society of NeuroInterventional Surgery's (SNIS) 23rd Annual Meeting that may improve the accuracy of delivering experimental therapies directly to malignant brain tumors.

A powerful new AI tool has uncovered what could be one of the biggest integrity problems in modern science. After analyzing 2.6 million cancer research papers published between 1999 and 2024, researchers identified more than 250,000 studies with writing patterns resembling papers suspected of being produced by fraudulent "paper mills."

Using computational tools, researchers from the Johns Hopkins Kimmel Cancer Center and the Johns Hopkins University School of Medicine have developed a method to predict which patients with a primary liver cancer called hepatocellular carcinoma (HCC) would most benefit from combination treatment using immunotherapy and a targeted therapy that blocks the growth signals that the tumor depends on.

The tool, based on histology only, may help identify patients most likely to benefit from triplet therapy.

Endoscopy-based deep learning algorithms demonstrate higher sensitivity and specificity than endoscopists overall in detecting early esophageal squamous cell carcinoma.

Cancer immunotherapy drugs known as immune checkpoint inhibitors (ICIs) can be miracle drugs for cancer patients, curing some and turning deadly disease into a manageable chronic condition in others.

Quibim, a global leader in precision imaging and artificial intelligence (AI) solutions, today announces the European and UK launch of QP-Breast®, its CE and UKCA-marked AI tool - the first in Europe and the United Kingdom to detect breast cancer using magnetic resonance imaging - which automatically identifies suspected lesions on breast MRI.

A machine learning algorithm predicts early-onset liver cancer in young adults better than traditional models.

Digestive system cancers, including hepatobiliary and gastrointestinal malignancies, remain a major global oncological burden. Although immune checkpoint inhibitors have changed the treatment landscape for some patients, their overall efficacy remains limited, with only about 15% to 30% of patients achieving durable clinical benefit.

Researchers from The University of Texas MD Anderson Cancer Center demonstrated that an artificial intelligence (AI)-based analysis of tumor biopsies can predict responses to immunotherapy in a study of patients with rare cancers, published in the Journal for ImmunoTherapy of Cancer.

Over the past two decades, high‑throughput sequencing has dramatically expanded our understanding of the gut microbiome's composition.

A human‑in‑the‑loop AI framework rapidly nominates CAR T targets, leading to a GPNMB‑directed CAR T cell with activity across melanoma, leukemia, and colorectal cancer in preclinical studies. The post AI Framework Surfaces New CAR T Target with Multi‑Cancer Potential appeared first on GEN - Genetic Engineering and Biotechnology News.

Using artificial intelligence (AI), researchers found that image-based risk scores for breast cancer derived from screening mammograms evolve over time and differ between women who develop cancer and those who do not, opening the door to a new era of dynamic breast cancer risk assessment.

Researchers at the UCLA Health Jonsson Comprehensive Cancer Center have developed a new platform that combines 3D bioprinting, advanced imaging and artificial intelligence to better monitor how cancer responds to treatment.

Tiny algae-based robots guided by magnets could improve bladder cancer treatment by boosting delivery of chemotherapy drugs into tumours, researchers say.

Endometrial cancer is the most common gynecologic cancer, with more than 69,000 cases diagnosed in the U.S. in 2025 and increasing up to 3% annually.

A multimodal AI model identified a subset of men with very high-risk localized prostate cancer who derived substantial benefit from adding abiraterone to standard therapy.

Physicians and researchers at the Netherlands Cancer Institute have developed an AI model that outperforms physicians at evaluating treatment response in pleural mesothelioma.

Cancer care is becoming too complex for any one clinician to track alone, making AI an increasingly valuable tool in modern oncology.

Experts in Heidelberg have developed an AI system that can classify brain tumors with unprecedented accuracy using standard microscopic tissue sections.

A new AI model reliably identifies hidden pancreatic ductal adenocarcinoma on routine CT scans months before the clinical diagnosis of the disease.

Three commercially available radiology AI systems have shown the potential to flag early signs of breast cancer up to 6 years before a diagnosis, according to a study published in Radiology, the flagship journal of the Radiological Society of North America (RSNA).

Women with abnormal mammograms often have to wait for weeks to find out whether they have breast cancer.

Online information about artificial intelligence (AI) and its impact on cancer research and treatment for both the patient and general-public audiences is limited, and the available webpages and videos are largely of low quality, difficult to read, and frequently omit risks of AI use, according to new research presented today at the at the 2026 American Society of Clinical Oncology (ASCO) Annual Meeting (Abstract 9000) and led by researchers from the Abramson Cancer Center (ACC) of the University of Pennsylvania and Penn's Perelman School of Medicine.

Researchers developed a spatial atlas of tertiary lymphoid structures across multiple cancer types, revealing how key features vary across tumor types and may influence patient outcomes. The post AI-Powered Pan-Cancer Map Reveals Tertiary Lymphoid Structures appeared first on GEN - Genetic Engineering and Biotechnology News.

In a new study published today in Science, researchers from The University of Texas MD Anderson Cancer Center developed a spatial atlas of specialized immune structures, called tertiary lymphoid structures (TLSs), across multiple cancer types.

Researchers at the Faculty of Information Technology at the University of Jyväskylä have used artificial intelligence to speed up the analysis of colorectal cancer samples and predict the functioning of the cells' DNA repair mechanism.

Researchers at University of California San Diego have developed a new artificial intelligence (AI) model that can translate a tumor's complex genetic profile into predictions about how that cancer may respond to treatment.

The Oncodarwinian Hypothesis (OdH) proposes a paradigm shift: cancer is not merely a disease but a potential macro‑immunoadaptive response – a self‑replicating algorithm that can be reprogrammed via AI‑based 3D printed p53 superproteins.

AI-driven genomic analysis could help researchers identify existing drugs that may be repurposed for subtype-specific breast cancer treatment. The review proposes an interpretability-driven framework that links multi-omics data, mechanistic validation, and clinical translation to make AI predictions more transparent and clinically useful.

Cancer patients who interact with an artificial intelligence (AI) avatar doctor before they meet their real-life consultant feel more knowledgeable and less stressed, according to research presented at the Congress of the European Society for Radiotherapy and Oncology (ESTRO 2026)

An AI technology is effective at planning the delivery of life-saving radiotherapy for cervical cancer and prostate cancer, according to results from a large international trial led by researchers at University College London (UCL) and the London School of Hygiene & Tropical Medicine (LSHTM).

Researchers at Rice University and the University of Texas MD Anderson Cancer Center have developed a compact, artificial

Researchers at Rice University and The University of Texas MD Anderson Cancer Center have developed a compact, artificial intelligence-powered imaging device that could transform how clinicians detect cancer.

Lung cancer remains one of the leading causes of cancer-related deaths worldwide, making early and accurate diagnosis essential for improving patient outcomes.

A team led by Cedars-Sinai Health Sciences University investigators has created a faster, cheaper way to determine the genes expressed in cancerous tumors.

While development is often described as a series of static snapshots of cell states, RegVelo models how these fate decisions are encoded in gene regulatory networks over time and space, and what drives cell state transitions. The post RegVelo AI Model Predicts Cell Fate, Tackles Developmental Disorders and Cancer appeared first on GEN - Genetic Engineering and Biotechnology News.

A new AI tool finds early hints of pancreatic cancer in CT scans that doctors would otherwise miss, an early test found.

Mayo Clinic's AI model identifies invisible signs of pancreatic cancer on routine CT scans, potentially transforming early detection and survival rates.

A new trial will test whether a tool that harnesses Apple Watch health data and artificial intelligence can help protect children undergoing cancer treatment from infections.

Artificial intelligence tools like ChatGPT are increasingly being explored in cancer care, but they can sometimes produce outdated or incorrect information. In medicine, where accuracy is critical, that risk is a serious concern.

An AI model (REDMOD) can pick up the very early subtle tissue changes of pancreatic ductal adenocarcinoma, the most common form of pancreatic cancer, which conventional imaging and the human eye find difficult to detect, finds research published online in the journal Gut.

The Mass General Brigham research team behind FaceAge, an artificial intelligence (AI) tool that can estimate a person's biological age from a single photo, is reporting in a new study that estimating biological age from multiple photos taken over time can provide even more information about how well a person with cancer will do with treatment.

A clinical trial designed to test an AI-based skin cancer detection app has some surprising results.

In a National Institutes of Health (NIH)-funded study, researchers developed a cancer assessment tool that can identify high-risk patients and the tumor

Using machine learning, an electronic nose can “smell” early signs of ovarian cancer in the blood. The method

Researchers at UC Berkeley and City of Hope, a cancer research and treatment organization, have created a novel

Researchers created a novel microfluidic platform, AI tool, and risk score that can assess women’s breast cancer risk at the cellular level, based on the how how breast cancer cells respond to being squeezed, and their “mechanical age.” The post AI Learns to Predict Breast Cancer Risk from How Single Cells Respond to Pressure appeared first on GEN - Genetic Engineering and Biotechnology News.

From organoid models to pathology, AI is increasingly embedded across cancer research areas. Fay Lin, PhD, and Jonathan D. Grinstein, PhD, discuss how challenges, such as adoption and trust continue to hinder AI's impact on patient outcomes. The post AACR 2026 Video Update: Cancer Research Edges Toward an AI-Driven Era appeared first on GEN - Genetic Engineering and Biotechnology News.