Empowering Biomedical Discovery with AI Agents

• URL: http://arxiv.org/abs/2404.02831v2
• Date: Wed, 24 Jul 2024 20:31:52 GMT
• Title: Empowering Biomedical Discovery with AI Agents
• Authors: Shanghua Gao, Ada Fang, Yepeng Huang, Valentina Giunchiglia, Ayush Noori, Jonathan Richard Schwarz, Yasha Ektefaie, Jovana Kondic, Marinka Zitnik,
• Abstract summary: We envision "AI scientists" as systems capable of skeptical learning and reasoning. Biomedical AI agents combine human creativity and expertise with AI's ability to analyze large datasets. AI agents can impact areas ranging from virtual cell simulation, programmable control of phenotypes, and the design of cellular circuits to developing new therapies.
• Score: 15.125735219811268
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We envision "AI scientists" as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate AI models and biomedical tools with experimental platforms. Rather than taking humans out of the discovery process, biomedical AI agents combine human creativity and expertise with AI's ability to analyze large datasets, navigate hypothesis spaces, and execute repetitive tasks. AI agents are poised to be proficient in various tasks, planning discovery workflows and performing self-assessment to identify and mitigate gaps in their knowledge. These agents use large language models and generative models to feature structured memory for continual learning and use machine learning tools to incorporate scientific knowledge, biological principles, and theories. AI agents can impact areas ranging from virtual cell simulation, programmable control of phenotypes, and the design of cellular circuits to developing new therapies.

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