Leveraging large language models for academic conference organization

Yuan Luo; Yikuan Li; Omolola Ogunyemi; Eileen Koski; Blanca E. Himes

doi:10.1038/s41746-025-01492-7

Leveraging large language models for academic conference organization

Yuan Luo^*, Yikuan Li, Omolola Ogunyemi, Eileen Koski, Blanca E. Himes

^*Corresponding author for this work

Preventive Medicine

Research output: Contribution to journal › Comment/debate › peer-review

Abstract

We piloted using Large Language Models (LLMs) for organizing AMIA 2024 Informatics Summit. LLMs were prompt engineered to develop algorithms for reviewer assignments, group presentations into sessions, suggest session titles, and provide one-sentence summaries for presentations. These tools substantially reduced planning time while enhancing the coherence and efficiency of conference organization. Our experience shows the potential of generative AI and LLMs to complement human expertise in academic conference planning.

Original language	English (US)
Article number	101
Journal	npj Digital Medicine
Volume	8
Issue number	1
DOIs	https://doi.org/10.1038/s41746-025-01492-7
State	Published - Dec 2025

ASJC Scopus subject areas

Medicine (miscellaneous)
Health Informatics
Computer Science Applications
Health Information Management

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AU - Koski, Eileen

AU - Himes, Blanca E.

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AB - We piloted using Large Language Models (LLMs) for organizing AMIA 2024 Informatics Summit. LLMs were prompt engineered to develop algorithms for reviewer assignments, group presentations into sessions, suggest session titles, and provide one-sentence summaries for presentations. These tools substantially reduced planning time while enhancing the coherence and efficiency of conference organization. Our experience shows the potential of generative AI and LLMs to complement human expertise in academic conference planning.

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Leveraging large language models for academic conference organization

Abstract

ASJC Scopus subject areas

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