Keeping generative artificial intelligence reliable in omics biology.
review · Level V
Where this comes from
- Record sourced from PubMed, PMID 41583973.
- Also identified by DOI 10.1016/j.patter.2025.101417 and PMC identifier 12827736.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
Generative artificial intelligence can be used to create realistic new data, even for complex real-world processes that cannot be exhaustively modeled: the model is simply learned from preexisting data. Generative artificial intelligence is therefore expected to be a game changer in omics research, where data collection is hampered by considerable experimental constraints. However, it can also "hallucinate"-i.e., create data that are too original to be realistic-which is a critical issue in molecular biology, as hallucinated inferences could have devastating consequences. The author thus explores various use cases to mitigate hallucination-induced risks and to safely unleash the full potential of generative methods.