Multimodal healthcare AI: identifying and designing clinically relevant vision-language applications for radiology
N Yildirim, H Richardson, MT Wetscherek… - Proceedings of the CHI …, 2024 - dl.acm.org
Recent advances in AI combine large language models (LLMs) with vision encoders that
bring forward unprecedented technical capabilities to leverage for a wide range of …
bring forward unprecedented technical capabilities to leverage for a wide range of …
[HTML][HTML] Opportunities and challenges in the application of large artificial intelligence models in radiology
Influenced by ChatGPT, artificial intelligence (AI) large models have witnessed a global
upsurge in large model research and development. As people enjoy the convenience by this …
upsurge in large model research and development. As people enjoy the convenience by this …
Advancing multimodal medical capabilities of Gemini
Many clinical tasks require an understanding of specialized data, such as medical images
and genomics, which is not typically found in general-purpose large multimodal models …
and genomics, which is not typically found in general-purpose large multimodal models …
Zero-shot ecg classification with multimodal learning and test-time clinical knowledge enhancement
Electrocardiograms (ECGs) are non-invasive diagnostic tools crucial for detecting cardiac
arrhythmic diseases in clinical practice. While ECG Self-supervised Learning (eSSL) …
arrhythmic diseases in clinical practice. While ECG Self-supervised Learning (eSSL) …
LLM-RadJudge: Achieving Radiologist-Level Evaluation for X-Ray Report Generation
Evaluating generated radiology reports is crucial for the development of radiology AI, but
existing metrics fail to reflect the task's clinical requirements. This study proposes a novel …
existing metrics fail to reflect the task's clinical requirements. This study proposes a novel …
Dia-LLaMA: Towards Large Language Model-driven CT Report Generation
Medical report generation has achieved remarkable advancements yet has still been faced
with several challenges. First, the inherent imbalance in the distribution of normal and …
with several challenges. First, the inherent imbalance in the distribution of normal and …
MAIRA-2: Grounded Radiology Report Generation
Radiology reporting is a complex task that requires detailed image understanding,
integration of multiple inputs, including comparison with prior imaging, and precise …
integration of multiple inputs, including comparison with prior imaging, and precise …
DeViDe: Faceted medical knowledge for improved medical vision-language pre-training
Vision-language pre-training for chest X-rays has made significant strides, primarily by
utilizing paired radiographs and radiology reports. However, existing approaches often face …
utilizing paired radiographs and radiology reports. However, existing approaches often face …
Merlin: A Vision Language Foundation Model for 3D Computed Tomography
L Blankemeier, JP Cohen, A Kumar… - arXiv preprint arXiv …, 2024 - arxiv.org
Over 85 million computed tomography (CT) scans are performed annually in the US, of
which approximately one quarter focus on the abdomen. Given the current radiologist …
which approximately one quarter focus on the abdomen. Given the current radiologist …
FineRadScore: A Radiology Report Line-by-Line Evaluation Technique Generating Corrections with Severity Scores
A Huang, O Banerjee, K Wu, EP Reis… - arXiv preprint arXiv …, 2024 - arxiv.org
The current gold standard for evaluating generated chest x-ray (CXR) reports is through
radiologist annotations. However, this process can be extremely time-consuming and costly …
radiologist annotations. However, this process can be extremely time-consuming and costly …