AI-driven multimodal models improve prediction of recurrence and survival in liver cancer, surpassing single-modality approaches in accuracy.
- Open access
AI in cancer detection
Moving in medRxiv, bioRxiv, Cancer Cell, Cancer Research, Nature Medicine, Journal of hepatology, Nature Cancer, PLOS Computational Biology.
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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
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
CROWN achieves over 95% accuracy in 48 cytology classification tasks, establishing a universal visual backbone 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
Delta-Marches uses generative AI to decode disease-related tissue morphological changes at subcellular resolution, revealing key features in renal carcinoma and colorectal tissues.
- Open access
- 1 cites
PathPrism enables interpretable spatial tissue representations that enhance biomarker discovery and clinical predictions from routine histopathology slides.
PathPrism identifies hundreds of spatial biomarkers predictive of survival and molecular alterations in colorectal cancer, advancing precision oncology with 7,000 patient data.
- Open access
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