Medical studies based on real-world data, like observational studies, are crucial for understanding diseases and treatments. However, they rely on assumptions that, if violated, can lead to flawed conclusions and misinformed public health decisions. A new research direction explores how large language models (LLMs) could act as "co-pilots" for researchers, helping to identify design flaws and improve the reliability of causal inferences. These AI co-pilots could leverage their vast knowledge base to spot potential biases early in the study design phase, effectively emulating the expertise of a large, interdisciplinary team. Researchers have demonstrated how an LLM can help refine causal questions, critique study design choices, and interpret complex results, using examples of historical studies that reached incorrect conclusions. LLMs can even analyze visual data like graphs, pointing out discrepancies that suggest bias. A key aspect of this "causal co-pilot" framework involves grounding the LLM in established causal inference principles and regulatory guidelines. This ensures the AI's recommendations align with best practices and contribute to the transparency of the study. While promising, challenges remain in developing this technology. Researchers need to refine training methods, incorporate expert feedback, and create ways to evaluate the performance of these AI co-pilots. Addressing these challenges will unlock the potential of LLMs to revolutionize medical research, improving the quality and reliability of studies that inform critical healthcare decisions.