Oncoformer accurately predicts cancer development within a year and diagnoses tumors using electronic health records and chest X-rays in Chinese and UK patients.
- In the news
AI in cancer detection
Moving in Cancer Discovery.
- Moved, week to 23 Oct 2026
- 1
- Papers in 60 days
- 22
Add AI in cancer detection as an interest in JClubFree on iPhone and Android · interests shape Home, Discover and your digest
Rebuilt
Moved this week
Latest in AI in cancer detection
Newest first · last 60 days
AI-driven multimodal models outperform single-modality approaches in predicting recurrence and survival in liver cancer patients.
- Open access
EAGLE AI model detects early esophageal cancer with 90% sensitivity and 98.5% specificity from chest CT scans across multiple centers, enabling scalable screening.
- Open access
Algorithm D achieved the highest AUROC of 0.849 and sensitivity across all AI-positive rates in a large US screening cohort, outperforming other commercial and open-source models.
General-purpose multimodal large language models in histopathology exhibit errors and hallucinations in over 80% of outputs, impacting diagnostic safety and reliability.
- Open access
Implementing EHR-based risk models with imaging and blood tests detects pancreatic cancer early in 8.2% of high-risk adults aged 50-84.
- Open access
CROWN achieves over 95% accuracy in 48 cytology patch-level classifications, establishing it as a highly effective universal model for computational cytopathology.
SmartHisto achieves a mean IoU of 0.75 in histology image segmentation, outperforming competitors by over 25% and reducing annotation needs significantly.
- Open access
Deep learning combined with label-free fluorescence lifetime imaging achieves a 0.966 accuracy in predicting EGFR mutations in lung adenocarcinoma tissues.
- Open access
CRISP, a clinically oriented foundation model trained on over 100,000 frozen sections, achieves 92.6% diagnostic accuracy in intraoperative pathology across diverse cases.
- Open access
Counterfactual diffusion models like MoPaDi enable interpretable explanations by revealing morphological features linked to AI predictions in pathology, with 4 datasets spanning multiple cancers.
- Open access
UMITIC accurately characterizes cellular phenotypes and tissue neighborhoods across diverse multiplex imaging datasets, achieving high agreement with expert annotations and biological structures.
- Open access
Add AI in cancer detection as an interest in JClub
Browse JClub
Journals this month
- bioRxiv6157
- medRxiv1716
- PNAS447
- Sci Adv354
- JAMA262
- Environ Res230
- BMJ200
- Environ Pollution165
- Nat Commun161
- Science141
- Nature138
- Cell Rep130
- Lancet128
- NEJM119
- Curr Biol108
- mBio107
- Plant Cell Environ107
- J Exp Bot106
Topics moving, week to 23 Oct 2026
- Single-cell and spatial transcriptomics6
- Neuroinflammation and Neurodegeneration Mechanisms5
- Bacteriophages and microbial interactions5
- Gut microbiota and health5
- Genomics and Phylogenetic Studies4
- Cancer Genomics and Diagnostics4
- Cancer Immunotherapy and Biomarkers4
- Alzheimer's disease research and treatments3
- Inflammatory Bowel Disease3
- Influenza Virus Research Studies3