Zobrazeno 1 - 10
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pro vyhledávání: '"Bitterman, P."'
Autor:
Chen, Canyu, Yu, Jian, Chen, Shan, Liu, Che, Wan, Zhongwei, Bitterman, Danielle, Wang, Fei, Shu, Kai
Large Language Models (LLMs) hold great promise to revolutionize current clinical systems for their superior capacities on medical text processing tasks and medical licensing exams. Meanwhile, traditional ML models such as SVM and XGBoost have still
Externí odkaz:
http://arxiv.org/abs/2411.06469
Autor:
Gao, Yanjun, Myers, Skatje, Chen, Shan, Dligach, Dmitriy, Miller, Timothy A, Bitterman, Danielle, Chen, Guanhua, Mayampurath, Anoop, Churpek, Matthew, Afshar, Majid
Large language models (LLMs) are being explored for diagnostic decision support, yet their ability to estimate pre-test probabilities, vital for clinical decision-making, remains limited. This study evaluates two LLMs, Mistral-7B and Llama3-70B, usin
Externí odkaz:
http://arxiv.org/abs/2411.04962
As Vision Language Models (VLMs) gain widespread use, their fairness remains under-explored. In this paper, we analyze demographic biases across five models and six datasets. We find that portrait datasets like UTKFace and CelebA are the best tools f
Externí odkaz:
http://arxiv.org/abs/2410.13146
Autor:
Matos, João, Chen, Shan, Placino, Siena, Li, Yingya, Pardo, Juan Carlos Climent, Idan, Daphna, Tohyama, Takeshi, Restrepo, David, Nakayama, Luis F., Pascual-Leone, Jose M. M., Savova, Guergana, Aerts, Hugo, Celi, Leo A., Wong, A. Ian, Bitterman, Danielle S., Gallifant, Jack
Multimodal/vision language models (VLMs) are increasingly being deployed in healthcare settings worldwide, necessitating robust benchmarks to ensure their safety, efficacy, and fairness. Multiple-choice question and answer (QA) datasets derived from
Externí odkaz:
http://arxiv.org/abs/2410.12722
Autor:
Chen, Shan, Gao, Mingye, Sasse, Kuleen, Hartvigsen, Thomas, Anthony, Brian, Fan, Lizhou, Aerts, Hugo, Gallifant, Jack, Bitterman, Danielle
Background: Large language models (LLMs) are trained to follow directions, but this introduces a vulnerability to blindly comply with user requests even if they generate wrong information. In medicine, this could accelerate the generation of misinfor
Externí odkaz:
http://arxiv.org/abs/2409.20385
Autor:
Yu, Huizi, Zhou, Jiayan, Li, Lingyao, Chen, Shan, Gallifant, Jack, Shi, Anye, Li, Xiang, Hua, Wenyue, Jin, Mingyu, Chen, Guang, Zhou, Yang, Li, Zhao, Gupte, Trisha, Chen, Ming-Li, Azizi, Zahra, Zhang, Yongfeng, Assimes, Themistocles L., Ma, Xin, Bitterman, Danielle S., Lu, Lin, Fan, Lizhou
Simulated patient systems play a crucial role in modern medical education and research, providing safe, integrative learning environments and enabling clinical decision-making simulations. Large Language Models (LLM) could advance simulated patient s
Externí odkaz:
http://arxiv.org/abs/2409.18924
Autor:
Wang, Xiaoye, Zhang, Nicole Xi, He, Hongyu, Nguyen, Trang, Yu, Kun-Hsing, Deng, Hao, Brandt, Cynthia, Bitterman, Danielle S., Pan, Ling, Cheng, Ching-Yu, Zou, James, Liu, Dianbo
Recent advancements in artificial intelligence (AI), particularly in deep learning and large language models (LLMs), have accelerated their integration into medicine. However, these developments have also raised public concerns about the safe applica
Externí odkaz:
http://arxiv.org/abs/2409.18968
Autor:
Gao, Yanjun, Myers, Skatje, Chen, Shan, Dligach, Dmitriy, Miller, Timothy A, Bitterman, Danielle, Churpek, Matthew, Afshar, Majid
The introduction of Large Language Models (LLMs) has advanced data representation and analysis, bringing significant progress in their use for medical questions and answering. Despite these advancements, integrating tabular data, especially numerical
Externí odkaz:
http://arxiv.org/abs/2408.11854
Autor:
Yang, Rui, Ning, Yilin, Keppo, Emilia, Liu, Mingxuan, Hong, Chuan, Bitterman, Danielle S, Ong, Jasmine Chiat Ling, Ting, Daniel Shu Wei, Liu, Nan
Generative artificial intelligence (AI) has brought revolutionary innovations in various fields, including medicine. However, it also exhibits limitations. In response, retrieval-augmented generation (RAG) provides a potential solution, enabling mode
Externí odkaz:
http://arxiv.org/abs/2406.12449
Autor:
Restrepo, David, Wu, Chenwei, Vásquez-Venegas, Constanza, Matos, João, Gallifant, Jack, Celi, Leo Anthony, Bitterman, Danielle S., Nakayama, Luis Filipe
The deployment of large language models (LLMs) in healthcare has demonstrated substantial potential for enhancing clinical decision-making, administrative efficiency, and patient outcomes. However, the underrepresentation of diverse groups in the dev
Externí odkaz:
http://arxiv.org/abs/2406.13152