  Evidence to guide healthcare decisions is often limited by a lack of relevant
and trustworthy literature as well as difficulty in contextualizing existing
research for a specific patient. Large language models (LLMs) could potentially
address both challenges by either summarizing published literature or
generating new studies based on real-world data (RWD). We evaluated the ability
of five LLM-based systems in answering 50 clinical questions and had nine
independent physicians review the responses for relevance, reliability, and
actionability. As it stands, general-purpose LLMs (ChatGPT-4, Claude 3 Opus,
Gemini Pro 1.5) rarely produced answers that were deemed relevant and
evidence-based (2% - 10%). In contrast, retrieval augmented generation
(RAG)-based and agentic LLM systems produced relevant and evidence-based
answers for 24% (OpenEvidence) to 58% (ChatRWD) of questions. Only the agentic
ChatRWD was able to answer novel questions compared to other LLMs (65% vs.
0-9%). These results suggest that while general-purpose LLMs should not be used
as-is, a purpose-built system for evidence summarization based on RAG and one
for generating novel evidence working synergistically would improve
availability of pertinent evidence for patient care.
