Zobrazeno 1 - 10
of 335
pro vyhledávání: '"Rousseau, P. F."'
Autor:
Zhang, Gongbo, Xu, Zihan, Jin, Qiao, Chen, Fangyi, Fang, Yilu, Liu, Yi, Rousseau, Justin F., Xu, Ziyang, Lu, Zhiyong, Weng, Chunhua, Peng, Yifan
While holding great promise for improving and facilitating healthcare, large language models (LLMs) struggle to produce up-to-date responses on evolving topics due to outdated knowledge or hallucination. Retrieval-augmented generation (RAG) is a pivo
Externí odkaz:
http://arxiv.org/abs/2412.15271
Autor:
Jin, Qiao, Chen, Fangyuan, Zhou, Yiliang, Xu, Ziyang, Cheung, Justin M., Chen, Robert, Summers, Ronald M., Rousseau, Justin F., Ni, Peiyun, Landsman, Marc J, Baxter, Sally L., Al'Aref, Subhi J., Li, Yijia, Chen, Alex, Brejt, Josef A., Chiang, Michael F., Peng, Yifan, Lu, Zhiyong
Publikováno v:
npj Digital Medicine, 2024
Recent studies indicate that Generative Pre-trained Transformer 4 with Vision (GPT-4V) outperforms human physicians in medical challenge tasks. However, these evaluations primarily focused on the accuracy of multi-choice questions alone. Our study ex
Externí odkaz:
http://arxiv.org/abs/2401.08396
A human decision-maker benefits the most from an AI assistant that corrects for their biases. For problems such as generating interpretation of a radiology report given findings, a system predicting only highly likely outcomes may be less useful, whe
Externí odkaz:
http://arxiv.org/abs/2305.19339
Publikováno v:
AMIA 2022 Annual Symposium
This paper applies multiple machine learning (ML) algorithms to a dataset of de-identified COVID-19 patients provided by the COVID-19 Research Database. The dataset consists of 20,878 COVID-positive patients, among which 9,177 patients died in the ye
Externí odkaz:
http://arxiv.org/abs/2303.00517
Publikováno v:
AMIA 2023 Informatics Summit
This paper applies eXplainable Artificial Intelligence (XAI) methods to investigate the socioeconomic disparities in COVID patient mortality. An Extreme Gradient Boosting (XGBoost) prediction model is built based on a de-identified Austin area hospit
Externí odkaz:
http://arxiv.org/abs/2302.08605
Deep neural networks (DNNs) have rapidly become a \textit{de facto} choice for medical image understanding tasks. However, DNNs are notoriously fragile to the class imbalance in image classification. We further point out that such imbalance fragility
Externí odkaz:
http://arxiv.org/abs/2212.02675
AI-powered Medical Imaging has recently achieved enormous attention due to its ability to provide fast-paced healthcare diagnoses. However, it usually suffers from a lack of high-quality datasets due to high annotation cost, inter-observer variabilit
Externí odkaz:
http://arxiv.org/abs/2210.08388
Autor:
Jaiswal, Ajay, Wang, Peihao, Chen, Tianlong, Rousseau, Justin F., Ding, Ying, Wang, Zhangyang
Despite the enormous success of Graph Convolutional Networks (GCNs) in modeling graph-structured data, most of the current GCNs are shallow due to the notoriously challenging problems of over-smoothening and information squashing along with conventio
Externí odkaz:
http://arxiv.org/abs/2210.08122
Autor:
Tang, Liyan, Goyal, Tanya, Fabbri, Alexander R., Laban, Philippe, Xu, Jiacheng, Yavuz, Semih, Kryściński, Wojciech, Rousseau, Justin F., Durrett, Greg
The propensity of abstractive summarization models to make factual errors has been studied extensively, including design of metrics to detect factual errors and annotation of errors in current systems' outputs. However, the ever-evolving nature of su
Externí odkaz:
http://arxiv.org/abs/2205.12854
Autor:
Jaiswal, Ajay, Li, Tianhao, Zander, Cyprian, Han, Yan, Rousseau, Justin F., Peng, Yifan, Ding, Ying
Computer-aided diagnosis plays a salient role in more accessible and accurate cardiopulmonary diseases classification and localization on chest radiography. Millions of people get affected and die due to these diseases without an accurate and timely
Externí odkaz:
http://arxiv.org/abs/2110.14787