# Keeping generative artificial intelligence reliable in omics biology

## TL;DR

This paper discusses how generative AI can revolutionize omics research but warns about the risks of unrealistic data generation and suggests ways to address them.

## Contribution

The paper introduces strategies to reduce hallucination risks in generative AI for reliable omics research.

## Key findings

- Generative AI can create realistic data for complex biological processes.
- Hallucinations in AI-generated data pose significant risks in molecular biology.
- Use cases are proposed to safely harness the potential of generative methods.

## 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.

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.