Last year saw significant progress in Large Language Models (LLMs) like ChatGPT, influencing various human activities. Neuroscientists must embrace LLMs’ potential, per a Neuron paper, as conditions are met for LLMs in neuroimaging, genetics, genomics, and clinical reports.
Unlike traditional research, LLMs absorb vast neuroscientific data, proposing specialized LLMs to break research silos. For drug development, collaboration between genetics-focused and neuroimaging LLMs is crucial, even if full understanding eludes scientists.
Realizing LLM potential demands enhanced infrastructure and a cultural shift toward data-driven science. Journals and public agencies must support AI-reliant studies. While traditional models persist, embracing LLM tech becomes vital for neurological advancements.
Lead author Danilo Bzdok notes neuroscientists are “drowning in information but starving for knowledge.” LLMs offer a solution by extracting, synthesizing, and potentially surpassing human comprehension in neuroscience domains.
Source NeuroScienceNews
