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Objective: This study quantifies the capabilities of GPT-3.5 and GPT-4 for clinical named entity recognition (NER) tasks and proposes task-specific prompts to improve their performance.
Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications
Savova GK, Masanz JJ, Ogren PV, Zheng J, Sohn S, Kipper-Schuler KC, et al · 2010
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Complexity-based prompting for multi-step reasoning
Fu Y, Peng H, Sabharwal A, Clark P, Khot T · 2010
Earlier work this paper cites.
Natural language processing: an introduction
Nadkarni PM, Ohno-Machado L, Chapman WW · 2011
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2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text
Uzuner Ö, South BR, Shen S, DuVall SL · 2011
Earlier work this paper cites.
A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries
Jiang M, Chen Y, Liu M, Rosenbloom ST, Mani S, Denny JC, et al · 2011
Earlier work this paper cites.
Mining electronic health records: towards better research applications and clinical care
Jensen PB, Jensen LJ, Brunak S · 2012
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Bidirectional LSTM-CRF models for sequence tagging
Huang Z, Xu W, Yu K · 2015
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Decoupled weight decay regularization
Loshchilov I, Hutter F · 2017
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Clinical natural language processing in languages other than English: opportunities and challenges
Névéol A, Dalianis H, Velupillai S, Savova G, Zweigenbaum P · 2018
Earlier work this paper cites.
Clinical information extraction applications: a literature review
Wang Y, Wang L, Rastegar-Mojarad M, Moon S, Shen F, Afzal N, et al · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin J, Chang MW, Lee K, Toutanova K · 2018
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Clinicalbert: Modeling clinical notes and predicting hospital readmission
Huang K, Altosaar J, Ranganath R · 2019
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Publicly available clinical BERT embeddings
Alsentzer E, Murphy JR, Boag W, Weng WH, Jin D, Naumann T, et al · 2019
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BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Lee J, Yoon W, Kim S, Kim D, Kim S, So CH, et al · 2020
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Language models are few-shot learners
Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, et al · 2020
Cited alongside, same era.
Transformers: State-of-the-art natural language processing
Wolf T, Debut L, Sanh V, Chaumond J, Delangue C, Moi A, et al · 2020
Cited alongside, same era.
Domain-specific language model pretraining for biomedical natural language processing
Gu Y, Tinn R, Cheng H, Lucas M, Usuyama N, Liu X, et al · 2021
Cited alongside, same era.
Extracting postmarketing adverse events from safety reports in the vaccine adverse event reporting system (VAERS) using deep learning
Du J, Xiang Y, Sankaranarayanapillai M, Zhang M, Wang J, Si Y, et al · 2021
Cited alongside, same era.
ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on Simplified Radiology Reports
Jeblick K, Schachtner B, Dexl J, Mittermeier A, Stüber AT, Topalis J, et al · 2022
Cited alongside, same era.
Large language models in biomedical natural language processing: benchmarks, baselines, and recommendations
Chen Q, Du J, Hu Y, Keloth VK, Peng X, Raja K, et al · 2023
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Genegpt: Augmenting large language models with domain tools for improved access to biomedical information
Jin Q, Yang Y, Chen Q, Lu Z · 2023
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Prompt engineering for healthcare: Methodologies and applications
Wang J, Shi E, Yu S, Wu Z, Ma C, Dai H, et al · 2023
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Exploring the effectiveness of prompt engineering for legal reasoning tasks
Yu F, Quartey L, Schilder F · 2023
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Prompt Engineering and Calibration for Zero-Shot Commonsense Reasoning
Ma C · 2023
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NCU-IISR: Prompt Engineering on GPT-4 to Stove Biological Problems in BioASQ 11b Phase B
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Wei J, Tay Y, Bommasani R, Raffel C, Zoph B, Borgeaud S, et al · 2022
Cited alongside, same era.
Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks
Chen W, Ma X, Wang X, Cohen WW · 2022
Cited alongside, same era.
A multitask, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity
Bang Y, Cahyawijaya S, Lee N, Dai W, Su D, Wilie B, et al · 2023
Cited alongside, same era.
Gpt-4 technical report
Achiam J, Adler S, Agarwal S, Ahmad L, Akkaya I, Aleman FL, et al · 2023
Cited alongside, same era.
How does CHATGPT perform on the United States Medical Licensing Examination? the implications of large language models for medical education and knowledge assessment
Gilson A, Safranek CW, Huang T, Socrates V, Chi L, Taylor RA, et al · 2023
Cited alongside, same era.
Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models
Kung TH, Cheatham M, Medenilla A, Sillos C, De Leon L, Elepaño C, et al · 2023
Cited alongside, same era.
Evaluating ChatGPT as an adjunct for radiologic decision-making
Rao A, Kim J, Kamineni M, Pang M, Lie W, Succi MD · 2023
Cited alongside, same era.
Hsueh CY, Zhang Y, Lu YW, Han JC, Meesawad W, Tsai RTH · 2023
Closest in time.
Is ChatGPT a Biomedical Expert?–Exploring the Zero-Shot Performance of Current GPT Models in Biomedical Tasks
Ateia S, Kruschwitz U · 2023
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Evaluation of ChatGPT Family of Models for Biomedical Reasoning and Classification
Chen S, Li Y, Lu S, Van H, Aerts HJ, Savova GK, et al · 2023
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Llama: Open and efficient foundation language models
Touvron H, Lavril T, Izacard G, Martinet X, Lachaux MA, Lacroix T, et al · 2023
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Llama 2: Open foundation and fine-tuned chat models
Touvron H, Martin L, Stone K, Albert P, Almahairi A, Babaei Y, et al · 2023
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The falcon series of open language models
Almazrouei E, Alobeidli H, Alshamsi A, Cappelli A, Cojocaru R, Debbah M, et al · 2023
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Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models
Sun J, Luo Y, Gong Y, Lin C, Shen Y, Guo J, et al · 2023
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Opportunities and challenges for ChatGPT and large language models in biomedicine and health
Tian S, Jin Q, Yeganova L, Lai PT, Zhu Q, Chen X, et al · 2024
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