Toward autonomous science with agentic artificial intelligence.

Lucie Y Guo¹, Darren S J Ting², Xinyi Su³, Alex Aliper⁴, Alex Zhavoronkov⁴, Daniel S W Ting⁵

¹F. M. Kirby Center, Scheie Eye Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.

²Academic Ophthalmology, Department of Inflammation and Ageing, School of Infection, Inflammation, and Immunology, College of Medicine and Health, University of Birmingham, Birmingham, UK; Birmingham and Midland Eye Centre, Sandwell and West Birmingham NHS Trust, Birmingham, UK; Singapore National Eye Center, Singapore Eye Research Institute, Singapore, Singapore; Duke-NUS Medical School, National University Singapore, Singapore, Singapore.

³Singapore National Eye Center, Singapore Eye Research Institute, Singapore, Singapore; Institute of Molecular Cell Biology, Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore; Department of Ophthalmology and Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University Hospital, Singapore, Singapore.

⁴Insilico Medicine, International Renewable Energy Agency HQ, Masdar City, Abu Dhabi, UAE.

⁵Singapore National Eye Center, Singapore Eye Research Institute, Singapore, Singapore; Duke-NUS Medical School, National University Singapore, Singapore, Singapore; Byers Eye Institute, Stanford University, Palo Alto, CA, USA. Electronic address: daniel.ting45@gmail.com.

PubMed DOI

Abstract

Artificial intelligence (AI) in biomedicine has evolved from pattern-recognition for medical image analysis to generative models like AlphaFold that predict protein structures. Tools such as Elicit, GPT-Rosalind, and Claude Science now bridge prediction and reasoning through literature synthesis, domain-tuned models, and agentic workbenches. Three recent systems (Co-Scientist, Robin, and Biomni) d…

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