Medical professionals frequently work in a data constrained setting to provide insights across a unique demographic. A few medical observations, for instance, informs the diagnosis and treatment of a patient. This suggests a unique setting for meta-learning, a method to learn models quickly on new tasks, to provide insights unattainable by other methods. We investigate the use of meta-learning and robustness techniques on a broad corpus of benchmark text and medical data. To do this, we developed new data pipelines, combined language models with meta-learning approaches, and extended existing meta-learning algorithms to minimize worst case loss. We find that meta-learning on text is a suitable framework for text-based data, providing better data efficiency and comparable performance to few-shot language models and can be successfully applied to medical note data. Furthermore, meta-learning models coupled with DRO can improve worst case loss across disease codes.
Pre-trained Language Models and Few-shot Learning for Medical Entity Extraction
Santa Clara University（圣克拉拉大学）；San Francisco State University（旧金山州立大学）；Cornell University（康奈尔大学）；The Chinese University of Hong Kong（香港中文大学）；Stevens Institute of Technology（史蒂文斯理工学院）
Assessing Large Language Models for Medical QA: Zero-Shot and LLM-as-a-Judge Evaluation
Department of Computer Science and Engineering, Islamic University of Technology, Gazipur, Bangladesh（计算机科学与工程系，伊斯兰技术大学，加兹ipur，孟加拉国）；Department of Computer Science（计算机科学系）；Engineering, Islamic University of Technology, Gazipur, Bangladesh（工程，伊斯兰技术大学，加兹ipur，孟加拉国）
Medical Incident Causal Factors and Preventive Measures Generation Using Tag-based Example Selection in Few-shot Learning
Graduate School of Information Science（信息科学研究生院）；Technology Hokkaido University Hokkaido, Japan（技术 Hokkaido 大学 Hokkaido, Japan）；National Institute of Information（信息国家研究所）；Faculty of Information Science（信息科学学院）
Optimizing Medical Question-Answering Systems: A Comparative Study of Fine-Tuned and Zero-Shot Large Language Models with RAG Framework
Department of Electrical Engineering；Computer Science University of Toledo Toledo, USA；Institute of Mathematical Sciences Claremont Graduate University Claremont, USA；Department of Bioengineering University of Toledo Toledo, USA；Department of Computer Science Bowling Green State University Bowling Green, USA
ZALM3: Zero-Shot Enhancement of Vision-Language Alignment via In-Context Information in Multi-Turn Multimodal Medical Dialogue
PAII Inc.（PAII公司）；Ping An Technology（平安科技）；Ping An Healthcare and Technology Company Limited（平安医疗健康科技有限公司）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Tianjin University（天津大学）
DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4
The University of Georgia（佐治亚大学）；Lehigh University（莱恩大学）；The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Massachusetts General Hospital and Harvard Medical School（麻省总医院和哈佛医学院）；Mayo Clinic（梅奥诊所）；ShanghaiTech University（上海科技大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能科技有限公司）；Shanghai Clinical Research and Trial Center（上海临床研究与试验中心）
When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications?
University of Colorado（科罗拉多大学）；University of Wisconsin Madison（威斯康星大学麦迪逊分校）；Mass General Brigham, Harvard Medical School（哈佛医学院麻省总医院）；Boston Children Hospital, Harvard Medical School（哈佛医学院波士顿儿童医院）；Loyola University Chicago（芝加哥洛约拉大学）；Dana-Farber Cancer Institute（达纳-法伯癌症研究所）
