Demographic bias in misdiagnosis by computational pathology models
Despite increasing numbers of regulatory approvals, deep learning-based computational
pathology systems often overlook the impact of demographic factors on performance …
pathology systems often overlook the impact of demographic factors on performance …
BERT in radiology: a systematic review of natural language processing applications
Abstract Introduction BERT (Bidirectional Encoder Representations from Transformers),
introduced in 2018, has revolutionized natural language processing (NLP). Its bidirectional …
introduced in 2018, has revolutionized natural language processing (NLP). Its bidirectional …
Chexagent: Towards a foundation model for chest x-ray interpretation
Chest X-rays (CXRs) are the most frequently performed imaging test in clinical practice.
Recent advances in the development of vision-language foundation models (FMs) give rise …
Recent advances in the development of vision-language foundation models (FMs) give rise …
Low-Rank Knowledge Decomposition for Medical Foundation Models
The popularity of large-scale pre-training has promoted the development of medical
foundation models. However some studies have shown that although foundation models …
foundation models. However some studies have shown that although foundation models …
Human-AI symbiosis: a path forward to improve chest radiography and the role of radiologists in patient care
To start, we need more rigorous testing of algorithms with prospective, pragmatic, real-world
clinical trials in diverse settings to assure robust generalizability, lack of biases, and a high …
clinical trials in diverse settings to assure robust generalizability, lack of biases, and a high …
SERVAL: Synergy Learning between Vertical Models and LLMs towards Oracle-Level Zero-shot Medical Prediction
Recent development of large language models (LLMs) has exhibited impressive zero-shot
proficiency on generic and common sense questions. However, LLMs' application on …
proficiency on generic and common sense questions. However, LLMs' application on …
Bias in foundation models: primum non nocere or caveat emptor?
J Czum, S Parr - Radiology: Artificial Intelligence, 2023 - pubs.rsna.org
Shane Parr, BS, is a 3rd-year PhD student under George Konidaris, PhD, at Brown
University studying artificial intelligence in the Department of Computer Science. His …
University studying artificial intelligence in the Department of Computer Science. His …
Unreading Race: Purging Protected Features from Chest X-ray Embeddings
Purpose: To analyze and remove protected feature effects in chest radiograph embeddings
of deep learning models. Materials and Methods: An orthogonalization is utilized to remove …
of deep learning models. Materials and Methods: An orthogonalization is utilized to remove …
Reconsidering Conclusions of Bias Assessment in Medical Imaging Foundation Models
AS Chaudhari, C Bluethgen, D Ouyang - Radiology: Artificial …, 2023 - pubs.rsna.org
Editor: In the November 2023 issue of Radiology: Artificial Intelligence, Dr Glocker and
colleagues report that a deep learning model for chest radiograph interpretation depicted …
colleagues report that a deep learning model for chest radiograph interpretation depicted …
Weak Supervision, Strong Results: Achieving High Performance in Intracranial Hemorrhage Detection with Fewer Annotation Labels
PhD student affiliated with The University of Texas Medical Scientist Training Program at
Houston. He is currently a postdoctoral fellow at The University of Texas MD Anderson …
Houston. He is currently a postdoctoral fellow at The University of Texas MD Anderson …