Peer-Reviewed Publication
J R Soc Interface2025;22(227):20240674.June 1, 2025Journal Article
Scientific hypothesis generation by large language models: laboratory validation in breast cancer treatment.
Abbi Abdel-Rehim1, Hector Zenil1,2,3,4,5, Oghenejokpeme Orhobor1, Marie Fisher6, Ross J Collins6, Elizabeth Bourne6, Gareth W Fearnley6, Emma Tate6, Holly X Smith6, Larisa N Soldatova7, Ross King1,8
1Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.
2Algorithmic Dynamics Lab, Research Departments of Biomedical Computing and Digital Twins, School of Biomedical Engineering and Imaging Sciences, King's Institute for AI, King's College London, London, England, UK.
3Oxford Immune Algorithmics, Oxford University Innovation and London Institute for Healthcare Engineering, London, England, UK.
4Cancer Interest Group, The Francis Crick Institute, London, England, UK.
5Defence and National Security, The Alan Turing Institute, British Library, London, England, UK.
6Arctoris Ltd, Oxford, UK.
7Computer Science, Goldsmiths University of London, London, UK.
8Department of Computer Science and Engineering, Chalmers University, Gothenburg, Sweden.
Abstract
Large language models (LLMs) have transformed artificial intelligence (AI) and achieved breakthrough performance on a wide range of tasks. In science, the most interesting application of LLMs is for hypothesis formation. A feature of LLMs, which results from their probabilistic structure, is that the output text is not necessarily a valid inference from the training text. These are termed 'halluci…
Create a free account to keep reading
Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.
Want unlimited research access? See Pro plans