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. As such, it offers the potential to shift an all too common late stage, terminal disease diagnosis to one that is at an early stage (stage 0) and treatable, say the researchers. While REDMOD was more accurate than experienced radiologists, it requires testing in high risk patients, defined as those with unexpected weight loss and newly diagnosed diabetes, before it can be widely used in clinical practice, they add.
AI Spots Pancreatic Cancer Years Before It Shows Up, Study Finds
An artificial intelligence system can spot pancreatic cancer long before it shows up on scans, raising the prospect of catching one of the deadliest tumors early enough to successfully treat, a study found.
Next-generation AI for visually occult pancreatic cancer detection in a low-prevalence setting with longitudinal stability and multi-institutional generalisability
Background Failure of conventional imaging to detect pancreatic ductal adenocarcinoma (PDA) at its visually occult pre-diagnostic stage is a primary barrier to improving its otherwise poor rate of survival. Objective To develop and validate the Radiomics-based Early Detection MODel (REDMOD), an AI framework to identify subvisual radiomic signatures of pre-diagnostic PDA on standard-of-care CT. Designs REDMOD was trained on a multi-institutional cohort (n=969; 156 pre-diagnostic, 813 control) and tested on an independent set (n=493; 63 pre-diagnostic, 430 control), simulating a low prevalence (~1:6) early detection paradigm. The fully automated framework couples AI-driven segmentation with a heterogeneous ensemble architecture trained on a 40-feature radiomic signature derived from Synthetic Minority Over-sampling Technique (SMOTE)-balanced data. A tunable Youden Index-optimised classification threshold enables performance calibration without retraining. Validation included direct comparison with radiologists, longitudinal test–retest analysis and external specificity validation across two independent cohorts (n=539 and n=80). Results On an independent test set (n=493), REDMOD identified occult PDA (AUC 0.82; 73.0% sensitivity) at a median 475-day lead time. This represented nearly twofold higher sensitivity than radiologists (38.9%; p<0.001), which grew to nearly threefold (68.0% vs 23.0%) at >24 months lead time. REDMOD showed strong longitudinal stability (90–92% concordance) and generalisable specificity across multi-institutional (81.3%; n=539) and public (87.5%; n=80) datasets. Mechanistic analyses confirmed predictive power derived principally from multi-scale wavelet-filtered textural features (90% of selected signature), which outperformed unfiltered features (AUC 0.82 vs 0.74; p=0.007) in capturing subvisual architectural disruptions. Conclusions REDMOD is an automated, mechanistically grounded, longitudinally stable, externally validated AI that surpasses radiologists for PDA detection at its visually occult pre-diagnostic stage. These attributes position it for prospective validation in high-risk cohorts, a necessary step towards shifting the paradigm from late-stage symptomatic diagnosis to proactive pre-clinical interception.
Using AI Made Doctors Worse at Spotting Cancer Without Assistance
A new study offers the latest evidence of potential “deskilling” effects on AI users.
Fred Hutch researchers test privacy-first AI platform for cancer research
Researchers at Fred Hutch Cancer Center are testing whether a collaborative AI research platform can accelerate the pace of cancer research leading to faster diagnoses and more precise, targeted therapies, especially for rare types of cancers while safeguarding patient privacy.
The algorithm will see you now - Works in Progress Magazine
Radiology combines digital images, clear benchmarks, and repeatable tasks. But replacing humans with AI is harder than it seems.
AI can detect heart disease in women using mammograms, study suggests
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Foundation Models in Medical Imaging: A Review and Outlook
Foundation models (FMs) are changing the way medical images are analyzed by learning from large collections of unlabeled data. Instead of relying on manually annotated examples, FMs are...
AI linked to explosion of low-quality biomedical research papers
Analysis flags hundreds of studies that seem to follow a template, reporting correlations between complex health conditions and single variables based on publicly available data sets.
It’s remarkably easy to inject new medical misinformation into LLMs
Changing just 0.001% of inputs to misinformation makes the AI less accurate.
Meta’s New AI Asked for My Raw Health Data—and Gave Me Terrible Advice
Meta’s Muse Spark model offers to analyze users’ health data, including lab results. Beyond the obvious privacy risks, it’s not a capable stand-in for a real doctor.
Amy Deng on Twitter / X
I’m an AI researcher turned brain tumor patient, and recently I used the models to crack my mystery fatigue faster than my PCP could. I believe everyone can do the same with their own symptoms. Here’s how: pic.twitter.com/0jhbPvEi7V— Amy Deng (@amydeng_) June 16, 2026
Using AI to help physicians diagnose rare genetic diseases affecting children
Researchers used an OpenAI reasoning model to help diagnose rare diseases, identifying 18 new diagnoses in previously unsolved cases.
Medical Algorithms Are Failing Communities Of Color
Medical algorithms routinely make decisions about patients’ health care, yet they are rife with bias. Health equity must be built into the development and deployment of these health algorithms.
GAN-Based Data Augmentation for Prediction Improvement Using Gene Expression Data in Cancer
Within the area of bioinformatics, Deep Learning (DL) models have shown exceptional results in applications in which histological images, scans and tomographies are used. However, when gene expression data is under analysis, the performance is often limited, further hampered by the complexity of these models that require several instances, in the order of thousands, to provide good results. Due to the difficulty and the costs involved in the collection of medical data, the application of Data Augmentation (DA) techniques to alleviate the lack of samples is a topic of great relevance. State-of-the-art models based on Conditional Generative Adversarial Networks (CGAN) and some introduced modifications are used in this work to investigate the effect of DA for prediction of the vital status of patients from RNA-Seq gene expression data. Experimental results on several real-world data sets demonstrate the effectiveness and efficiency of the proposed models. The application of DA methods significantly increase prediction accuracy, leading by 12% with respect to benchmark data sets and 3.15% with respect to data processed with feature selection. Results based on CGAN models outperform in most cases, alternative methods like the SMOTE or noise injection techniques.
#Pancreaticcancer is usually caught too late to treat. A new study finds an #AI system can pick up early warning signs in routine CT scans more than a year before diagnosis -- outperforming radiologists on scans previously read as normal. Hopefully it can improve survival. shorturl.at/zXUwj
AI Spots Pancreatic Cancer Years Before It Shows Up, Study Finds
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