ReviewNature biotechnology2026
Agentic AI and the rise of in silico team science in biomedical research.
Review in Nature biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Agentic AI in oncology: autonomous workflows for clinical decision support and evidence synthesis.Annals of medicine and surgery (2012) · 2026Article
- Synthetic nucleic acids in a post-agent biosecurity Era.Frontiers in bioengineering and biotechnology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Agentic artificial intelligence (AI) systems are emerging as teams of intelligent computational experts capable of rivaling human performance in labor-intensive tasks, including literature review, hypothesis formulation, data analysis and model interpretation. These systems are poised to accelerate labor-intensive biomedical research by making autonomous decisions based on contextual information and expert feedback. Agentic AI systems have been developed for various applications, including drug discovery, data analysis and biomarker identification; however, several distinct challenges remain for making these systems broadly deployable in biomedical research. Here we discuss three key algorithms and seven foundational building-block characteristics that contribute to the development of agentic AI systems. We highlight their biomedical applications, design considerations and the challenges and opportunities associated with deploying agentic AI systems to advance collaborative scientific research.
Indexed as
Identifiers
41735549What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.