arXiv Computer Vision August 25, 2026 By Amy Rafferty, Rishi Ramaesh, Ajitha Rajan

Radiologist-Guided Causal Concept Bottleneck Models for Chest X-Ray Interpretation

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arXiv Machine Learning

Jun 29

Revisiting Performance Claims for Chest X-Ray Models Using Clinical Context

arXiv:2509. 19671v3 Announce Type: replace Abstract: Public datasets of Chest X-Rays (CXRs) have long been a popular benchmark for developing machine learning (ML) computer vision models in healthcare.

By Andrew Wang, Jiashuo Zhang, Michael Oberst

computer-vision benchmarks

arXiv AI

Sep 10

A radiographic world model for clinical reasoning and evidence generation

The paper introduces MedDream, a radiographic world model that learns a shared continuous latent state from paired chest radiograph-text observations. MedDream outperforms existing diagnostic and generative AI models across eight clinical datasets, improving diagnostic reasoning, resident concordance, and evidence generation. Targeted synthetic augmentation guided by subgroup performance gaps further enhances model performance, particularly for Asian patients.

By Suyang Xi, Songtao Hu, Shansong Wang, Mojtaba Safari, Luke del Balzo, Ehsan Ul Karim, Mingzhe Hu, Kuo Zhang, Tonghe Wang, Ralph R. Weichselbaum, Xiaofeng Yang

arXiv Machine Learning

Jul 30

Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance

arXiv:2607. 26333v1 Announce Type: cross Abstract: Chest X-ray (CXR) machine learning relies heavily on automated evaluation using reference standards that aim to approximate clinical judgment.

By Panagiotis Fytas, Ian Selby, Clemens Karner, Judith Babar, Simon Baker, Jake Beckford, Timothy J. Sadler, Shahab Shahipasand, Arthikkaa Thavakumar, John Li Chen, Alex Sawer, Michael Roberts, Jonathan Weir-McCall, J. H. F. Rudd, Carola-Bibiane Sch\"onlieb, Anna Korhonen, Anna Breger

llms fine-tuning multimodal safety

arXiv AI

Jun 2

MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution

arXiv:2604. 26283v3 Announce Type: replace-cross Abstract: High-precision medical diagnosis relies not only on static imaging features but also on the implicit diagnostic memory experts instantly invoke during image interpretation.

By Chunzheng Zhu, Jiaqi Zeng, Junyu Jiang, Jianxin Lin, Yijun Wang

agents nlp reinforcement-learning efficiency benchmarks safety

arXiv Machine Learning

Jun 5

Symb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathology

arXiv:2606. 06224v1 Announce Type: cross Abstract: Explanations of multiple instance learning (MIL) models are widely used for validation and discovery in digital histopathology.

By Yanqing Luo (Berlin Institute for the Foundations of Learning and Data, Berlin, Germany, Machine Learning Group, Technische Universit\"at Berlin, Berlin, Germany), Julius Hense (Berlin Institute for the Foundations of Learning and Data, Berlin, Germany, Machine Learning Group, Technische Universit\"at Berlin, Berlin, Germany), Niklas Preni{\ss}l (Institute of Pathology, Charit\'e Universit\"atsmedizin, Berlin, Germany, Berlin Institute of Health at Charit\'e -- Universit\"atsmedizin Berlin, BIH Biomedical Innovation Academy, BIH Charit\'e Digital Clinician Scientist Program, Berlin, Germany), Andreas Mock (Institute of Pathology, Ludwig Maximilian University of Munich, Munich, Germany, Division of Translational Medical Oncology, DKFZ, Heidelberg, Germany, NCT Heidelberg, Heidelberg, Germany, German Cancer Consortium), Klaus-Robert M\"uller (Berlin Institute for the Foundations of Learning and Data, Berlin, Germany, Machine Learning Group, Technische Universit\"at Berlin, Berlin, Germany, Department of Artificial Intelligence, Korea University, Seoul, Korea, Max-Planck Institute for Informatics, Saarbr\"ucken, Germany), Thomas Schnake (Department of Chemistry, Chemical Physics Theory Group, University of Toronto, Canada, Vector Institute for Artificial Intelligence, Toronto, Canada, Acceleration Consortium, University of Toronto, Canada), Mina Jamshidi Idaji (Berlin Institute for the Foundations of Learning and Data, Berlin, Germany, Machine Learning Group, Technische Universit\"at Berlin, Berlin, Germany)

safety

arXiv AI

Aug 25

CDEG: Learning Decision-Critical Evidence for Long-Horizon Diagnostic Agents

arXiv:2608.22899v1 Announce Type: new Abstract: Unlike static medical question answering, long-horizon diagnosis captures the sequential nature of clinical practice: evidence is progressively acquire...

By Xiwei Dai, Zijie Meng, Zhiting Fan, Yixuan Tang, Ziru Niu, Zuozhu Liu

agents nlp benchmarks

One email a morning, machine-written

Related stories

Revisiting Performance Claims for Chest X-Ray Models Using Clinical Context

A radiographic world model for clinical reasoning and evidence generation

Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance

MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution

Symb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathology

CDEG: Learning Decision-Critical Evidence for Long-Horizon Diagnostic Agents