activity
20232026
most citedAdapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization

755 citations · 979 across the 14 of their papers we have counts for

collaborators

14 papers

cs.CV2026

Comp2Comp: Open-Source Software with FDA-Cleared Artificial Intelligence Algorithms for Computed Tomography Image Analysis

Adrit Rao, Malte Jensen, Andrea T. Fisher +28

Artificial intelligence allows automatic extraction of imaging biomarkers from already-acquired radiologic images. This paradigm of opportunistic imaging adds value to medical imag…

cs.CV2025

Explaining 3D Computed Tomography Classifiers with Counterfactuals

Joseph Paul Cohen, Louis Blankemeier, Akshay Chaudhari

Counterfactual explanations enhance the interpretability of deep learning models in medical imaging, yet adapting them to 3D CT scans poses challenges due to volumetric complexity…

eess.IV2025

MedVAE: Efficient Automated Interpretation of Medical Images with Large-Scale Generalizable Autoencoders

Maya Varma, Ashwin Kumar, Rogier van der Sluijs +7

Medical images are acquired at high resolutions with large fields of view in order to capture fine-grained features necessary for clinical decision-making. Consequently, training d…

cs.LG2024★ 90 cited

Foundation Models in Radiology: What, How, When, Why and Why Not

Magdalini Paschali, Zhihong Chen, Louis Blankemeier +6

Recent advances in artificial intelligence have witnessed the emergence of large-scale deep learning models capable of interpreting and generating both textual and imaging data. Su…

cs.CV2024

Time-to-Event Pretraining for 3D Medical Imaging

Zepeng Huo, Jason Alan Fries, Alejandro Lozano +6

With the rise of medical foundation models and the growing availability of imaging data, scalable pretraining techniques offer a promising way to identify imaging biomarkers predic…

cs.CL2024★ 5 cited

Overview of the First Shared Task on Clinical Text Generation: RRG24 and "Discharge Me!"

Justin Xu, Zhihong Chen, Andrew Johnston +9

Recent developments in natural language generation have tremendous implications for healthcare. For instance, state-of-the-art systems could automate the generation of sections in…

cs.CV2024

Automated detection of underdiagnosed medical conditions via opportunistic imaging

Asad Aali, Andrew Johnston, Louis Blankemeier +6

Abdominal computed tomography (CT) scans are frequently performed in clinical settings. Opportunistic CT involves repurposing routine CT images to extract diagnostic information an…

cs.CV2024★ 33 cited

Merlin: A Computed Tomography Vision-Language Foundation Model and Dataset

Louis Blankemeier, Ashwin Kumar, Joseph Paul Cohen +37

The large volume of abdominal computed tomography (CT) scans coupled with the shortage of radiologists have intensified the need for automated medical image analysis tools. Previou…

cs.CL2024★ 42 cited

GREEN: Generative Radiology Report Evaluation and Error Notation

Sophie Ostmeier, Justin Xu, Zhihong Chen +8

Evaluating radiology reports is a challenging problem as factual correctness is extremely important due to the need for accurate medical communication about medical images. Existin…

cs.CV2024★ 26 cited

A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation

Zhihong Chen, Maya Varma, Justin Xu +20

Over 1.4 billion chest X-rays (CXRs) are performed annually due to their cost-effectiveness as an initial diagnostic test. This scale of radiological studies provides a significant…

cs.CV2023

Identifying Spurious Correlations using Counterfactual Alignment

Joseph Paul Cohen, Louis Blankemeier, Akshay Chaudhari

Models driven by spurious correlations often yield poor generalization performance. We propose the counterfactual (CF) alignment method to detect and quantify spurious correlations…

cs.CL2023★ 755 cited

Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization

Dave Van Veen, Cara Van Uden, Louis Blankemeier +16

Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time. Although large langua…

cs.CL2023★ 19 cited

MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records

Scott L. Fleming, Alejandro Lozano, William J. Haberkorn +27

The ability of large language models (LLMs) to follow natural language instructions with human-level fluency suggests many opportunities in healthcare to reduce administrative burd…

cs.CV2023★ 9 cited

Comp2Comp: Open-Source Body Composition Assessment on Computed Tomography

Louis Blankemeier, Arjun Desai, Juan Manuel Zambrano Chaves +11

Computed tomography (CT) is routinely used in clinical practice to evaluate a wide variety of medical conditions. While CT scans provide diagnoses, they also offer the ability to e…