Our ML research team brings experience from institutions including Stanford, the University of Washington, and other leading centers of AI innovation. Together, we’ve published in NeurIPS, ICLR, CVPR, ACL, Nature Medicine, and other top venues, advancing the science behind trustworthy medical imaging AI.

(* indicated equal contribution, in bold are HOPPR team members)

ML Research

Image showing Agentic systems in radiology

TRoVe: Discovering Error-Inducing Static Feature Biases in Temporal Vision-Language Models

Thirty-ninth Conference on Neural Information Processing Systems - 2025

Maya Varma, Jean-Benoit Delbrouck, Sophie Ostmeier, Akshay S Chaudhari, Curtis Langlotz

[paper]

Recognize Any Surgical Object: Unleashing the Power of Weakly-Supervised Data

The Thirteenth International Conference on Learning Representations

Jiajie Li, Brian R Quaranto, Chenhui Xu, Ishan Mishra, Ruiyang Qin, Dancheng Liu, Peter C W Kim, Jinjun Xiong

[paper]

Agentic Systems in Radiology: Design, Applications, Evaluation, and Challenges

ArXiv preprint 2510.09404

Christian Bluethgen, Dave Van Veen, Daniel Truhn, Jakob Nikolas Kather, Michael Moor, Malgorzata Polacin, Akshay S Chaudhari, Thomas Frauenfelder, Curtis P Langlotz, Michael Krauthammer, Farhad Nooralahzadeh

[paper ]

IMAGE showing proposed architecture of measuring Faithfulness in Medical Vision Language Models

Evaluating Reasoning Faithfulness in Medical Vision-Language Models using Multimodal Perturbations

Proceedings of the 5th Machine Learning for Health Symposium – 2025

Johannes Moll, Markus Graf, Tristan Lemke, Nicolas Lenhart, Daniel Truhn, Jean-Benoit Delbrouck, Jiazhen Pan, Daniel Rueckert, Lisa C. Adams, Keno K. Bressem

[paper]

[code]

RadEval: A framework for radiology text evaluation

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations - 2025

Justin Xu, Xi Zhang, Javid Abderezaei, Julie Bauml, Roger Boodoo, Fatemeh Haghighi, Ali Ganjizadeh, Eric Brattain, Dave Van Veen, Zaiqiao Meng, David W Eyre, Jean-Benoit Delbrouck

[paper]

[code]

diagram showing large language models in radiology

Best Practices for Large Language Models in Radiology

Radiology Volume 315, No. 1 - 2025

Christian Bluethgen, Dave Van Veen, Cyril Zakka, Katherine E. Link, Aaron Hunter Fanous, Roxana Daneshjou, Thomas Frauenfelder, Curtis P Langlotz, Sergios Gatidis, Akshay S. Chaudhari

[paper]

Structuring Radiology Reports: Challenging LLMs with Lightweight Models

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing – 2025

Johannes Moll, Louisa Fay, Asfandyar Azhar, Sophie Ostmeier, Tim Lueth, Sergios Gatidis, Curtis Langlotz, Jean-Benoit Delbrouck

[paper]

[code]

SMMILE: An Expert-Driven Benchmark for Multimodal Medical In-Context Learning

Thirty-ninth Conference on Neural Information Processing Systems Datasets and Benchmarks Track - 2025

Melanie Rieff, Maya Varma, Ossian Rabow, Subathra Adithan, Julie Kim, Ken Chang, Hannah Lee, Nidhi Rohatgi, Christian Bluethgen, Mohamed S Muneer, Jean-Benoit Delbrouck*, Michael Moor*

* Equal authorship

[paper]

[code]

[project page]

Image showing best practices for safe LLM and GenAI in Radiology

Best practices for the safe use of large language models and other generative AI in radiology

Radiology Volume 316, No. 3 - 2025

Paul H Yi, Hana L. Haver, Jean J. Jeudy, Woojin Kim, Felipe C. Kitamura, Eniola T Oluyemi, Andrew D Smith, Linda Moy, Vishwa S Parekh

[paper]

Image showing structured radiology report generation

Automated Structured Report Generation

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) - 2025

Jean-Benoit Delbrouck, Justin Xu, Johannes Moll, Alois Thomas, Zhihong Chen, Sophie Ostmeier, Asfandyar Azhar, Kelvin Zhenghao Li, Andrew Johnston, Christian Bluethgen, Eduardo Pontes Reis, Mohamed S Muneer, Maya Varma, and Curtis Langlotz

[paper]

Methodological Challenges in Evaluating Large Language Models in Radiology

Radiology Volume 316, No. 3 - 2025

David Li, Woojin Kim, and Paul H. Yi

[paper]

image summarizing dataset and benchmarks with adapted LLMs

A Dataset and Benchmark for Hospital Course Summarization with Adapted Large Language Models

Journal of the American Medical Informatics Association (JAMIA) Volume 32, No. 3

Asad Aali, Dave Van Veen, Yamin Ishraq Arefeen, Jason Hom, Christian Bluethgen, Eduardo P Reis, Sergios Gatidis, Namuun Clifford, Joseph Daws, Arash S. Tehrani, Jangwon Kim, Akshay S. Chaudhari

[paper]