Yasha Wang on Muck Rack

Yasha Wang

As seen in: Nature, MDPI
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Articles

ClinicRealm: Re-evaluating large language models with conventional machine learning for non-generative clinical prediction tasks

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By Yinghao Zhu, Junyi Gao, Zixiang Wang, Weibin Liao, Xiaochen Zheng, Lifang Liang, Miguel O. Bernabeu, Yasha Wang, Lequan Yu, Chengwei Pan, Ewen Harrison, Liantao Ma
| Nature Verified
Abstract Large Language Models (LLMs) are increasingly deployed in medicine. However, their utility for non-generative clinical prediction is under-evaluated, and they are often assumed to be inferior to specialized models, creating potential for misuse and misunderstanding.
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PRIMARY-AI: outcomes-based standards to safeguard primary care in the AI era - Nature Medicine

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By Dian Zeng, Lorainne Tudor Car, Kamlesh Khunti, Yuanli Liu, Till Bärnighausen, Niels H. Chavannes, Pearse A. Keane, Holger Kunz, Lan Xue, Yih Chung Tham, Lorenzo Righetto Verified, Donald J. Boudreau, Qionghai Dai, Weiping Jia, Yang Liu Verified, Dinggang Shen, Jia Liu, Weixing Shen, John S. Ji, Zhong Lin Wang, Zhiyi Wang, Haibo Wang, Shenglan Tang, Chenyang Pei, Zehua Jiang, Zihao Zou, Yiming Qin, Huating Li, Yasha Wang, Dinesh Visva Gunasekeran, Sabrina Wong, Dong Xu, Ryan J. Urbanowicz, Liliana Laranjo, Ana Neves, Nan Liu, Neil M. Bressler, Rifat Atun, David C. Klonoff, Nigam H. Shah, Josip Car, Rupa Sarker, Phuoc Van Le
| Nature Verified
Artificial intelligence (AI) is being deployed at scale in primary care — from diagnostic support and risk stratification to triage and clinical documentation — without any agreed-upon outcomes-based framework for determining whether it strengthens or undermines continuity, coordination, comprehensiveness and people-centered care1. This is dangerous for the billions of people whose first contact with health systems is primary care.
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ConfAgents: A Conformal-Guided Multi-Agent Framework for Cost-Efficient Medical Diagnosis

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By Yinghao Zhu, Zixiang Wang, Yasha Wang, Junyi Gao
| arxiv.org
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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