A recent discussion on August 24, 2026, featuring Jungwon Byun, co-founder of Elicit, explored the evolving role of artificial intelligence in scientific research. Byun highlighted how AI tools are being developed specifically to assist scientists in identifying new hypotheses, mapping research fields, and making evidence-based decisions, moving beyond general question-answering capabilities.
Elicit, an AI platform, is designed for researchers and emphasizes rigorous citation and evidence. Unlike general chatbots, Elicit links all its responses to published research papers, clinical trials, or real data. This feature is crucial for scientists who need to verify information and guard against AI hallucinations, which can be subtle and difficult to detect.
Byun described how a research and development director at a major pharmaceutical company utilized Elicit to analyze approximately 16,000 oncology drugs. This analysis helped the director understand areas of concentration, well-validated findings, and opportunities for new research, aiding in strategic decisions about resource allocation and research direction. The platform assists in evaluating trade-offs between well-understood biology and more novel, higher-risk areas.
The co-founder also discussed the challenge of AI sycophancy, where models tend to agree with user input. Elicit incorporates evaluations to prevent this, aiming to provide objective analysis. The platform also helps scientists assess the quality of data within research papers by analyzing methodologies and statistical techniques, allowing researchers to override AI suggestions based on their expertise.
Byun expressed optimism about AI’s potential to accelerate scientific discovery, particularly in areas like drug development and clinical trials, by automating and streamlining many manual processes. While acknowledging the physical world’s friction in certain scientific endeavors, she believes that the continuous pursuit of solutions will eventually lead to significant breakthroughs, such as curing diseases or solving global energy issues.
The term elicit, in the context of AI, refers to both drawing out the capabilities of a model and understanding the goals of the human user. Elicit aims to make AI thinking consistent across different experiences, which is vital for serious scientific applications like drug testing where reproducibility and clear documentation are essential for auditing and regulatory compliance.
