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The recent success of large language models (LLMs) has paved the way for their adoption in the high-stakes domain of healthcare.
Generating medical logic modules for clinical trial eligibility
C. G. Parker · 2005
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A systematic review highlights threats to validity in studies of barriers to cancer trial participation
D Fayter, C McDaid, and A Eastwood · 2007
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Recruiting minorities where they receive care: Institutional barriers to cancer clinical trials recruitment in a safety-net hospital
G. Joseph and D. Dohan · 2009
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Electronic screening improves efficiency in clinical trial recruitment
Samir R. Thadani, Chunhua Weng, J. Thomas Bigger, John F. Ennever, and David Wajngurt · 2009
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Automated matching software for clinical trials eligibility: Measuring efficiency and flexibility
L. Penberthy, R. Brown, F. Puma, et al · 2010
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Elixr: an approach to eligibility criteria extraction and representation
Chunhua Weng, Xiaoying Wu, Zhihui Luo, Mary Regina Boland, Dimitri Theodoratos, and Stephen B Johnson · 2011
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Enrollment performance: weighing the ’facts’
K. Getz · 2012
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Effort required in eligibility screening for clinical trials
L. T. Penberthy, B. A. Dahman, V. I. Petkov, et al · 2012
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Case-based reasoning using electronic health records efficiently identifies eligible patients for clinical trials
Riccardo Miotto and Chunhua Weng · 2015
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Predicting low accrual in the national cancer institute’s cooperative group clinical trials
CS Bennette et al · 2016
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Opportunities and challenges in leveraging electronic health record data in oncology
M Berger, M Curtis, G Smith, J Harnett, and A Abernethy · 2016
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Challenges in recruitment and retention of clinical trial subjects
RA Kadam, SU Borde, SA Madas, SS Salvi, and SS Limaye · 2016
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A test collection for matching patients to clinical trials
Bevan Koopman and Guido Zuccon · 2016
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Tufts analysis: Patient recruitment shortcomings laid at feet of poor provider, researcher engagement, 2017
Association of Clinical Research Professionals · 2017
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Assessing study start-up practices, performance, and perceptions among sponsors and contract research organizations
M. Lamberti, M. Wilkinson, B. Harper, C. Morgan, and K. Getz · 2018
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Cohort selection for clinical trials using deep learning models
I. Segura-Bedmar and P. Raez · 2019
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Criteria2query: a natural language interface to clinical databases for cohort definition
C. Yuan, P. B. Ryan, C. Ta, Y. Guo, Z. Li, J. Hardin, R. Makadia, P. Jin, N. Shang, T. Kang, and C. Weng · 2019
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Language models are few-shot learners
T. Brown et al · 2020
Cited alongside, same era.
Overview of the trec 2021 clinical trials track
Kirk Roberts, Dina Demner-Fushman, Ellen M. Voorhees, Steven Bedrick, and William R. Hersh · 2021
Cited alongside, same era.
Palm: Scaling language modeling with pathways
A. Chowdhery et al · 2022
Cited alongside, same era.
Combining human and machine intelligence for clinical trial eligibility querying
Y. Fang, B. Idnay, Y. Sun, H. Liu, Z. Chen, K. Marder, H. Xu, R. Schnall, and C. Weng · 2022
Cited alongside, same era.
Cross-task generalization via natural language crowdsourcing instructions
Swaroop Mishra, Daniel Khashabi, Chitta Baral, and Hannaneh Hajishirzi · 2022
Cited alongside, same era.
Neural query synthesis and domain-specific ranking templates for multi-stage clinical trial matching
Ronak Pradeep, Yilin Li, Yuetong Wang, and Jimmy Lin · 2022
Capabilities of gpt-4 on medical challenge problems
H. Nori, N. King, S. M. McKinney, D. Carignan, and E. Horvitz · 2023
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Instruction tuning with gpt-4
Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, and Jianfeng Gao · 2023
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, and Chelsea Finn · 2023
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A self-learning resource-efficient re-ranking method for clinical trials search
Maciej Rybinski, Vincent Nguyen, and Sarvnaz Karimi · 2023
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Large language models encode clinical knowledge
K. Singhal, S. Azizi, T. Tu, S. S. Mahdavi, J. Wei, H. W. Chung, N. Scales, A. Tanwani, H. Cole-Lewis, S. Pfohl, and et al · 2023
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Evaluating large language models on medical evidence summarization
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Cited alongside, same era.
Overview of the TREC 2022 clinical trials track
Kirk Roberts, Dina Demner-Fushman, Ellen M. Voorhees, Steven Bedrick, and William R. Hersh · 2022
Cited alongside, same era.
Large language models are few-shot clinical information extractors
Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim, and David Sontag · 2023
Cited alongside, same era.
How (not) to ensemble lvlms for vqa
Lisa Alazraki, Lluis Castrejon, Mostafa Dehghani, Fantine Huot, Jasper Uijlings, and Thomas Mensink · 2023
Cited alongside, same era.
Constitutional ai: Harmlessness from ai feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, et al · 2023
Cited alongside, same era.
Scaling instruction-finetuned language models
Hyung W. Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, et al · 2023
Cited alongside, same era.
Medalign: A clinician-generated dataset for instruction following with electronic medical records
Scott L. Fleming, Alejandro Lozano, William J. Haberkorn, Jenelle A. Jindal, Eduardo P. Reis, Rahul Thapa, Louis Blankemeier, et al · 2023
Cited alongside, same era.
L. Tang, Z. Sun, B. Idnay, J. G. Nestor, A. Soroush, P. A. Elias, Z. Xu, Y. Ding, G. Durrett, J. F. Rousseau, C. Weng, and Y. Peng · 2023
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Alpaca: A strong, replicable instruction-following model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B Hashimoto · 2023
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Opportunities and challenges for chatgpt and large language models in biomedicine and health
Shubo Tian, Qiao Jin, Lana Yeganova, Po-Ting Lai, Qingqing Zhu, Xiuying Chen, Yifan Yang, Qingyu Chen, Won Kim, Donald C Comeau, et al · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, et al · 2023
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Zephyr: Direct distillation of lm alignment
Lewis Tunstall, Edward Beeching, Nathan Lambert, Nazneen Rajani, Kashif Rasul, Younes Belkada, Shengyi Huang, et al · 2023
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Clinical text summarization: Adapting large language models can outperform human experts
D. Van Veen, C. Van Uden, L. Blankemeier, J. B. Delbrouck, A. Aali, C. Bluethgen, A. Pareek, M. Polacin, E. P. Reis, A. Seehofnerová, N. Rohatgi, P. Hosamani, W. Collins, N. Ahuja, C. P. Langlotz, J. Hom, S. Gatidis, J. Pauly, and A. S. Chaudhari · 2023
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Gpt-4: a new era of artificial intelligence in medicine
E. Waisberg, J. Ong, M. Masalkhi, et al · 2023
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Self-instruct: Aligning language models with self-generated instructions
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannaneh Hajishirzi · 2023
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Enhancing phenotype recognition in clinical notes using large language models: Phenobcbert and phenogpt
Jingye Yang, Cong Liu, Wendy Deng, Da Wu, Chunhua Weng, Yunyun Zhou, and Kai Wang · 2023
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Instruction tuning for large language models: A survey
Shengyu Zhang, Linfeng Dong, Xiaoya Li, Sen Zhang, Xiaofei Sun, Shuhe Wang, Jiwei Li, et al · 2023
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A survey of large language models in medicine: Progress, application, and challenge
Hongjian Zhou, Boyang Gu, Xinyu Zou, Yiru Li, Sam S Chen, Peilin Zhou, Junling Liu, Yining Hua, Chengfeng Mao, Xian Wu, et al · 2023
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