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.

AI model detects very early normally ‘invisible’ tissue changes of pancreatic cancer

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.

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.

Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: a multicentre, observational study

Continuous exposure to AI might reduce the ADR of standard non-AI assisted colonoscopy, suggesting a negative effect on endoscopist behaviour.

Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: a multicentre, observational study

Continuous exposure to AI might reduce the ADR of standard non-AI assisted colonoscopy, suggesting a negative effect on endoscopist behaviour.

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.

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GraphDx: A Cost-Aware Knowledge-Enhanced Multi-Agent Framework for Sequential Diagnosis

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Doctors’ AI scribes get names of drugs and diagnoses wrong, NHS watchdog warns

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Doctor forced to apologise after AI makes 'scary' error about illegal drugs

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Experts warn AI is making your brain work less

Generative AI tools have become hugely popular but some experts worry about the effect they have on the brain.

In the US, Caution Rules on AI Ahead of K-12 School Year

Students and teachers across the country are unlikely to find consistent, clear rules about AI use, reports Tech Policy Press fellow Chris Mills Rodrigo.

AI May Be Atrophying Our Brains, Professor Warns

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Companies That Replaced Humans With AI Are Realizing Their Mistake

As AI agents have yet to pay for themselves, more and more executives are waking up to the sloppy reality of AI hype.