Combining Insights From Multiple Large Language Models Improves Diagnostic Accuracy
This addresses the need for reliable diagnostic support tools in healthcare by improving accuracy and reducing vendor dependence, though it is incremental as it builds on existing collective intelligence methods.
The study tackled the problem of low diagnostic accuracy in individual large language models (LLMs) by aggregating responses from multiple LLMs, resulting in an average accuracy increase from 59.0% to 75.3% on a dataset of 200 clinical vignettes.
Background: Large language models (LLMs) such as OpenAI's GPT-4 or Google's PaLM 2 are proposed as viable diagnostic support tools or even spoken of as replacements for "curbside consults". However, even LLMs specifically trained on medical topics may lack sufficient diagnostic accuracy for real-life applications. Methods: Using collective intelligence methods and a dataset of 200 clinical vignettes of real-life cases, we assessed and compared the accuracy of differential diagnoses obtained by asking individual commercial LLMs (OpenAI GPT-4, Google PaLM 2, Cohere Command, Meta Llama 2) against the accuracy of differential diagnoses synthesized by aggregating responses from combinations of the same LLMs. Results: We find that aggregating responses from multiple, various LLMs leads to more accurate differential diagnoses (average accuracy for 3 LLMs: $75.3\%\pm 1.6pp$) compared to the differential diagnoses produced by single LLMs (average accuracy for single LLMs: $59.0\%\pm 6.1pp$). Discussion: The use of collective intelligence methods to synthesize differential diagnoses combining the responses of different LLMs achieves two of the necessary steps towards advancing acceptance of LLMs as a diagnostic support tool: (1) demonstrate high diagnostic accuracy and (2) eliminate dependence on a single commercial vendor.