Reliable clot-volume quantification and subsequent risk assessment in pulmonary embolism depend on precise segmentation of emboli on computed tomography pulmonary angiography. Deep learning models for this task must be trained on accurate voxel-level labels. The three public datasets that provide such labels were annot...
Qihang Sun, Zhong-Xiao Liu, Bailiang Jian et al.· 0 citations
A framework in which an AI builder turns a scientific brief into an executable AI lab: a task-specific solver that integrates models, knowledge resources, tools, and a controller behind a fixed interface is presented.
Jin-Ge Wu, Hong-Jian Zhou, Ming Zeng et al.· 0 citations
Latent diffusion models now dominate medical image generation, and every such pipeline rests on a \emph{tokenizer} that compresses images into the latent codes for image generation to operate on. Thereby, the tokenizer choice bounds every downstream task from reconstruction fidelity and generation quality to the repres...
Niklas Bubeck, Yundi Zhang, Vasiliki Sideri-Lampretsa et al.· 0 citations
MedRSI is introduced, the first recursive self-improvement framework for medicine, which continuously transforms diagnostic failures into new clinical capabilities through tool composition and task-specific model training, and shows that medical agents need not remain constrained by capabilities specified before deploy...
Jun-De Wu, Jia-Yuan Zhu, Min-Hao Hu et al.· 0 citations
This work presents a training-free agentic pipeline for automated brain MRI analysis and demonstrates that agentic AI can solve highly neuro-radiological image analysis tasks through tool use without the need for training or fine-tuning.
Ayhan Can Erdur, Daniel Scholz, Jiazhen Pan et al.· arXiv.org· 3 citations· ⚡1
PhysAssistBench is introduced, a benchmark for interactive doctor-patient-EHR assistance that uses a scalable pipeline to construct agentic patients: interactive, record-grounded agents that turn static EHR records into multi-turn clinical scenarios while preserving clinical factuality.
T. Du, Peijie Yu, Sihan Shang et al.· arXiv.org· 0 citations
Findings suggest that AI can be easily persuaded by what people say, who says it, and how the opinion is presented, enabling its safe and reliable use in high stakes medical decision making.
Jia-Yuan Zhu, Jia-Zhen Pan, Feng-Lin Liu et al.· 0 citations
Under matched backbone and inference settings, J-CoT-Zero matches or exceeds the strongest evaluated latent-reasoning baseline on every benchmark, while J-CoT-Train obtains the highest score across the evaluated mathematical, scientific, coding, and structured path-reasoning tasks.
Jun-De Wu, Jiayuan Zhu, Feng-Lin Liu et al.· arXiv.org· 1 citation
A Dynamic, Automatic and Systematic red-teaming audit framework that continuously stress-tests LLMs for health across four safety-critical axes: robustness, privacy, bias and hallucination, which provides a scalable framework for surfacing latent risks before such systems are deployed in consumer-facing health assistan...
Jiazhen Pan, Bailiang Jian, Paul Hager et al.· Nature Health· 0 citations
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