Empowering Biomedical Discovery with AI Agents
This work addresses the problem of slow and labor-intensive biomedical discovery for researchers by proposing an incremental framework that combines human creativity with AI capabilities.
The paper tackles the challenge of accelerating biomedical research by proposing 'AI scientists' as collaborative agents that integrate AI models with biomedical tools and experimental platforms, aiming to enhance discovery workflows through skeptical learning, reasoning, and self-assessment without replacing human expertise.
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.