Generative AI is changing how scientists study biology. It can help find drug candidates, design proteins, study cells, and manage large sets of data. But the technology also has a serious risk. AI can create false results that look real. These errors are often called AI hallucinations. They happen when an AI system produces an answer that seems correct but is not supported by real evidence. In biology, the problem could be much more serious than a wrong answer in a chat. A false AI result could affect a drug study or change how scientists understand a disease. Thomas Burger, a computational biologist at Grenoble Alpes University in France, has examined this issue. In an Opinion article in the journal Patterns, he looked at 10 possible uses of generative AI in biological research. His work shows that not all AI uses have the same level of risk. One important factor is how scientists use the AI output. If the system only suggests an idea, researchers can test that idea in a real lab. This creates a chance to catch an error before it becomes a scientific claim. Drug screening is one example. AI can examine large numbers of possible drug compounds. It can then help researchers choose which ones deserve more study. If the AI makes a mistake, a useful drug may be missed. Researchers may also spend time testing a drug that does not work. Those errors can waste money and time. Yet the final answer still depends on real tests. Protein screening can work in a similar way. AI may help researchers find proteins that could have useful features. Scientists must then test those proteins to learn if the predicted effects are real. The risk becomes greater when AI creates data that researchers use as evidence. Synthetic biological data can have many uses. It may help fill gaps in research data. It can also help protect private patient information. In some studies, synthetic data may reduce costs or lower the need for animal testing. However, false information inside synthetic data can create a major problem. Imagine that an AI system adds a biological feature that was not present in the original data. Researchers may not know that the feature is artificial. They could then treat it as a real biological signal. This could lead to a false discovery. The problem may also happen when AI works with real data. Scientists often use complex tools to measure genes, proteins, and other molecules. The raw results then pass through several computer steps before researchers draw a biological conclusion. AI may become part of many of those steps. A small error could change a signal during this process. That change may then affect later analysis. By the end, the final result could appear reasonable even though the original information was altered. This makes some AI errors hard to detect. The concern is not simply that AI could invent a completely fake discovery. A more difficult problem may occur when real scientific data is changed during an AI supported process. That change could make a real signal look stronger, weaker, or different from what it was. Generative AI can still offer major benefits to biological research. It can help scientists explore large data sets and find ideas faster. It may also support new work in drug development and protein design. But AI results need careful checks. Scientists can compare AI outputs with original data. They can repeat important tests. They can also use laboratory experiments to confirm major findings. These steps are especially important when AI generated information is used as evidence rather than as a simple research idea. The key lesson is clear. AI can help scientists search for discoveries, but it should not replace proof. A result that looks convincing is not enough. In biology, real experiments remain essential for showing whether an AI suggested effect actually exists.