NIH VideoCast - Overview of ChatGPT and other Large Language Models and their applications in Biomedicine

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Overview of ChatGPT and other Large Language Models and their applications in Biomedicine

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Air date:	Tuesday, May 23, 2023, 2:00:00 PM Time displayed is Eastern Time, Washington DC Local
Views:	Total views: 751 (417 Live, 334 On-demand)
Category:	Special
Runtime:	01:05:14
Closed captioning:	Check box to display CC outside of the video Note: You can drag the captioning window around and resize it
Description:	Abstract: The release of ChatGPT and the subsequent launch of GPT-4 by OpenAI has created a storm, capturing the attention of both the general public and domain professionals. In this talk, we will provide a comprehensive review of Large Language Models (LLMs), and how they can be used in Biomedical and Clinical applications, as well as their potential in addressing current challenges in the field, in driving innovation, and in improving the outcomes. About the Speakers: Dr. Shubo Tian is a research scientist in Dr. Zhiyong Lu’s group. He has extensive experience in using pre-trained language models for various biomedical and clinical applications, including information retrieval, information extraction such as named entity recognition and relation extraction, entity linking, and health outcome predictions. Dr. Shubo Tian holds a PhD degree in statistics and has a wide range of experience in the industry. Dr. Qiao Jin is researcher scientist in the BioNLP group led by Dr. Zhiyong Lu at NCBI/NLM/NIH. He received his M.D. degree from Tsinghua University in 2022. Dr. Jin’s research interests include deep learning, natural language processing, information retrieval, and their applications in biomedicine. He published ~20 peer-reviewed articles at EMNLP, NAACL, SIGIR, including BioELMo (one of the first pre-trained language models in biomedicine) and PubMedQA (a widely-used biomedical question answering benchmark for evaluating LLMs). He has won the first BioBank Disease AI Challenge, and the TREC 2020 Precision Medicine track. His EBM-Net work received the Best NLP Paper Award from the International Medical Informatics Association in 2021. His primary focus recently has been to improve biomedical information access with large language models.

NLM Title:	Overview of chatGPT and other large language models and their applications in biomedicine / Dr. Shubo Tian, Dr. Qiao Jin.
Author:	Tian, Shubo. Jin, Qiao. National Institutes of Health (U.S.). Text Mining and Natural Language Processing (NLP) Scientific Interest Group,
Publisher:
Abstract:	(CIT): Abstract: The release of ChatGPT and the subsequent launch of GPT-4 by OpenAI has created a storm, capturing the attention of both the general public and domain professionals. In this talk, we will provide a comprehensive review of Large Language Models (LLMs), and how they can be used in Biomedical and Clinical applications, as well as their potential in addressing current challenges in the field, in driving innovation, and in improving the outcomes. About the Speakers: Dr. Shubo Tian is a research scientist in Dr. Zhiyong Lu"s group. He has extensive experience in using pre-trained language models for various biomedical and clinical applications, including information retrieval, information extraction such as named entity recognition and relation extraction, entity linking, and health outcome predictions. Dr. Shubo Tian holds a PhD degree in statistics and has a wide range of experience in the industry. Dr. Qiao Jin is researcher scientist in the BioNLP group led by Dr. Zhiyong Lu at NCBI/NLM/NIH. He received his M.D. degree from Tsinghua University in 2022. Dr. Jin"s research interests include deep learning, natural language processing, information retrieval, and their applications in biomedicine. He published ~20 peer-reviewed articles at EMNLP, NAACL, SIGIR, including BioELMo (one of the first pre-trained language models in biomedicine) and PubMedQA (a widely-used biomedical question answering benchmark for evaluating LLMs). He has won the first BioBank Disease AI Challenge, and the TREC 2020 Precision Medicine track. His EBM-Net work received the Best NLP Paper Award from the International Medical Informatics Association in 2021. His primary focus recently has been to improve biomedical information access with large language models.
Subjects:	Medical Informatics Applications Models, Theoretical Natural Language Processing
Publication Types:	Lecture Webcast

NLM Classification:	W 26.55.A7
NLM ID:	9918625742706676
CIT Live ID:	49795
Permanent link:	https://videocast.nih.gov/watch=49795

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