Prompt Engineering - How to Write Prompts That Produce Evidence-Based Clinical Summaries
One of the highest-value uses of AI for clinicians is generating summaries of clinical evidence: literature reviews, guideline summaries, drug mechanism overviews, comparative treatment analyses. Done well, these can save hours of reading time and accelerate clinical education and research preparation. Done poorly, they produce confident misinformation that is more dangerous than no summary at all.
This course is specifically about how to prompt AI to produce clinical summaries that are accurate, appropriately qualified, and genuinely useful, rather than the kind of fluent but unreliable output that gives medical AI a bad reputation.
Why clinical summary prompts fail most often
The most common failure mode in AI clinical summaries is not that the AI produces content that is obviously wrong. It is that the AI produces content that is partly right, presented with uniform confidence regardless of the quality of the underlying evidence. A summary that treats a Class A recommendation from a well-powered RCT and an expert opinion based on limited data with the same confident prose voice is not useful for clinical decision-making. It is potentially misleading.
The second common failure is hallucinated citations. Ask for a summary with supporting references and you will reliably get some that are fabricated. The summary may be substantively accurate while the references cited are entirely fictional. This is a particularly dangerous combination because it creates the appearance of an evidence base that does not exist.
