Natural language processing (NLP) in the biomedical domain focuses on understanding domain-specific language and aims to facilitate the dissemination and exchange of information critical to medicine, healthcare, and public health.
The NLP Working Group focuses on computational linguistic methods, including but not limited to traditional machine learning models, deep learning models, transformer models, large language models, generative and agentic artificial intelligence; all sub-domains of biomedical NLP applications, including but not limited to information extraction, information retrieval, text classification, automatic text summarization, text mining, question answering, and chatbots; broader applications involving biomedical language, including but not limited to phenotyping, health risk assessment, clinical decision support, and intelligence healthcare agents. This may involve processing diverse biomedical texts, including but not limited to electronic health records, clinical narratives, patient portal data (e.g., MyChart message), scientific literature, and gray literature, such as drug labels, clinical guidelines, clinical trials, regulatory documents, and social media text.
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Slack
Areas of interest to the NLP WG include, but are not limited to:
- Development of LLMs and healthcare foundation models using biomedical text
- Methods to efficiently use foundation models for biomedical tasks, such as prompt and instruction tuning, retrieval-augmented generation, reinforcement learning, and agentic AI.
- Representation, extraction, normalization, and summarization of biomedical texts
- Methods to ensure safe and trustworthy NLP, such as bias analysis and mitigation, safety surveillance, and healthcare ethics and equity.
- Multimodal AI models integrate language data with other modalities, such as imaging, sensor and genomics.
- Development of tools and approaches to biomedical text understanding
- Applications of NLP in real-world practice in medicine, healthcare, and public health
- Addressing the broad needs of stakeholders from healthcare providers, researchers, and consumers.
Mission
The mission of the AMIA Natural Language Processing Working Group is to facilitate communication, collaboration, training, and networking for all stakeholders who develop, apply, and promote natural language processing in biomedical science, patient care, public health, and biomedical education.
Vision
The ultimate goal of Natural Language Processing, an area of artificial intelligence, is to enable computers to analyze and use natural language at the level required for biomedical tasks. The NLP-WG vision for NLP in the biomedical domain is to support biomedical sciences, patient care, public health, and biomedical education in a valuable manner.
Goals
AMIA NLP-WG aims to foster biomedical NLP research and applications, build a community of biomedical NLP researchers and practitioners, and establish connections among the researchers and practitioners from different scientific backgrounds and countries.
Working Group Activities
Past, Present and Future
- Working group calls
- Face to face meetings at the fall Annual Symposium and the spring Joint Summits or Informatics Amplify
- Pre-symposium workshop or meetings
- Monthly webinars
- Community engaged activities
- Collaboration with groups both inside and outside AMIA
- Identification and dissemination of relevant funding opportunities
- Industry-academic collaborations
- Academic guidance or career panel for early-stage researchers, e.g. doctoral students, postdoctoral researchers, junior faculty
Leadership
Rui Zhang, PhD, FACMI, FAMIA, FIAHSI, FAIMBE
Yonghui Wu, PhD
Xinsong Du, Ph.D.
Sujani Kakumanu, MD
Jakir Hossain Bhuiyan Masud, PhD
- Performing: Working Group has high level of engagement and output (workshops, papers, webinars)
- Networking: Working Group has internal and external networking opportunities for members (mentorship programs, social events, collaboration)
- Developing: New Working Group or revitalizing efforts to grow membership (recruitment efforts, leadership)