5 Strategies to Reduce AI Hallucination in Clinical Research | Seong min Cho

Large language models (LLMs) are increasingly used in clinical research to support tasks such as policy monitoring, data management, protocol review, and scientific writing. These tools can improve efficiency, organize complex information, and support decision-making. However, they also introduce an important risk: AI hallucination.

Hallucination occurs when an AI model produces information that sounds accurate and credible but is unsupported, misleading, or incorrect. In clinical research settings, this risk matters because inaccurate outputs may affect data quality, regulatory interpretation, documentation, and operational decisions.

Reducing hallucination requires more than simply using a “better” model. Current research suggests that reliability improves when model-level advances are combined with careful user-level strategies, including structured prompts, clear task boundaries, reasoning checks, and external tools.

Let’s take a look at five approaches that, while unable to eliminate AI hallucination, can make AI output significantly more transparent, verifiable, and appropriate for high-stakes research environments.

1. Step-by-Step Reasoning: Chain-of-Thought

One approach to improving reliability is chain-of-thought prompting, which encourages the model to work through a task step by step before producing a final answer. Research has shown that models often perform better on complex reasoning tasks when they are guided to generate intermediate reasoning rather than jumping directly to a conclusion.

In clinical research, this strategy may be useful when defining variables, interpreting statistical findings, reviewing study procedures, or summarizing pharmacokinetic (PK) analysis. Step-by-step prompts help slow down premature conclusions and make assumptions easier to identify. For example, a user might prompt: “Conduct a structured PK analysis by explicitly defining variables, outlining each analytical step, and providing a final interpretation supported only by the data.”
 

2. Better Alignment With Human Intent: Reinforcement Learning From Human Feedback

Hallucination is not only a reasoning problem. It can also occur when the model misunderstands the user’s goal. Reinforcement learning from human feedback (RLHF), used in models such as InstructGPT, has been shown to improve models' ability to follow instructions and produce responses judged more helpful and truthful.

At the user level, this principle can be applied by asking the model to evaluate its own response before finalizing it, for example, “Summarize relevant FDA and ICH guidance for decentralized trials, then critically review and revise the summary to remove unsupported or non-verifiable statements.”

3. Clear Instructions and Constrained Prompts

Clear instructions are one of the most practical ways to reduce hallucination. When a prompt is vague, the model may fill in missing details with assumptions. When a prompt clearly states what to include, what to exclude, and what format to use, the model has less room to invent information.

This is especially important in regulated clinical research tasks, such as data cleaning, documentation, protocol review, and PK reporting. A constrained prompt might state the following: “Develop a clinical data cleaning plan using only the provided dataset variables, formatted as structured steps, without introducing assumptions or external elements.”

4. Checking Multiple Reasoning Paths

Another strategy is to ask the model to compare multiple reasoning paths. Research on self-consistency shows that model outputs can improve when multiple reasoning chains are generated and the most consistent conclusion is selected.

This matters because a single reasoning pathway can still be flawed, even when it appears logical. In clinical research, seeking multiple interpretations can be helpful when reviewing statistical results, evaluating competing explanations, or determining whether a conclusion is actually supported by the data. This could be prompted as: “Provide two independent interpretations of these regression results and select the most consistent conclusion based only on the data.”

5. Using Tools Instead of Text Alone

Some hallucinations occur when models try to perform calculations or procedural tasks through text generation alone. Program-aided approaches show that LLMs can improve reliability by generating code and using external tools, such as Python, to complete calculations.

This is notable in clinical research as many tasks involve numerical summaries, derived variables, structured tables, and pharmacokinetic calculations. Instead of asking the model to estimate or simulate results in text, users can ask it to rely on computation: “Generate and apply Python-based calculations for PK parameter estimation using the provided data, and report only results directly derived from the computation.”

In Conclusion: Why Prompt Design Still Matters

No single method can fully eliminate AI hallucination. However, it has been demonstrated that reliability improves when several strategies are combined: step-by-step reasoning, alignment with user intent, constrained prompts, multiple reasoning checks, and external computational tools.

The key lesson is that AI should be treated as decision support, not final authority. LLMs can help organize information, accelerate routine tasks, and improve workflow efficiency, but expert review remains essential. And this is the approach we follow at Altasciences. We use AI responsibly and intentionally to support our scientists, strengthen decision-making, and allow teams to focus on higher-value scientific and strategic work.

AI serves as a force multiplier for human expertise, not a replacement for it. In regulated environments, such as our own, where accuracy, traceability, and documentation are critical, every AI-generated output is verified against source materials, study data, and applicable regulatory guidance.

By combining AI-enabled efficiency with scientific rigor and human oversight, organizations can realize the benefits of AI while maintaining the quality and integrity required for clinical research.

Discover more about Altasciences’ commitment to the responsible use of AI, or get in touch with one of our experts.

About the Author: Seong min Cho, RN, PMH-BC, CPHQ, PhD Candidate

Seong min Cho, RN, Clinical Research Nurse

Seong min Cho has been a Clinical Research Nurse at Altasciences since 2025, supporting clinical research operations, data and sample collection, and protocol-driven research activities. His background in behavioral health and biostatistics has been a strong influence on his interest in connecting clinical expertise with data-driven approaches to improve research quality and workforce outcomes. Connect with Seong min on LinkedIn.

CITATIONS AND FURTHER READING

The following publications informed this discussion and provide additional background on large language models, reasoning techniques, and their applications in clinical research and drug development:

• Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, Y., Wang, X., Dehghani, M., & Brahma, S. (2024). Scaling instruction-finetuned language models. Journal of Machine Learning Research, 25(70), 1–53.
• Gao, L., Madaan, A., Zhou, S., Alon, U., Liu, P., Yang, Y., Callan, J., & Neubig, G. (2023). Pal: Program-aided language models. International Conference on machine learning.
• Lin, A., Wang, Z., Jiang, A., Chen, L., Qi, C., Zhu, L., Mou, W., Gan, W., Zeng, D., & Xiao, M. (2025). Large language models in clinical trials: applications, technical advances, and future directions. BMC medicine, 23(1), 563. https://doi.org/10.1186/s12916-025-04348-9
• Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., & Ray, A. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35, 27730–27744.
• Tosca, E. M., Aiello, L., De Carlo, A., & Magni, P. (2025). Pharmacometrics in the Age of Large Language Models: A Vision of the Future. Pharmaceutics, 17(10), 1274. https://doi.org/10.3390/pharmaceutics17101274
• Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., & Zhou, D. (2022). Self-consistency improves chain of thought reasoning in language models. arXiv preprint arXiv:2203.11171.
• Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35, 24824–24837.