Emulating Human Cognitive Processes for Expert-Level Medical
  Question-Answering with Large Language Models
Khushboo Verma Marina Moore Stephanie Wottrich Karla Robles López Nishant Aggarwal Zeel Bhatt Aagamjit Singh Bradford Unroe Salah Basheer Nitish Sachdeva Prinka Arora Harmanjeet Kaur Tanupreet Kaur Tevon Hood Anahi Marquez Tushar Varshney Nanfu Deng Azaan Ramani Pawanraj Ishwara Maimoona Saeed Tatiana López Velarde Peña Bryan Barksdale Sushovan Guha Satwant Kumar
In response to the pressing need for advanced clinical problem-solving tools
in healthcare, we introduce BooksMed, a novel framework based on a Large
Language Model (LLM). BooksMed uniquely emulates human cognitive processes to
deliver evidence-based and reliable responses, utilizing the GRADE (Grading of
Recommendations, Assessment, Development, and Evaluations) framework to
effectively quantify evidence strength. For clinical decision-making to be
appropriately assessed, an evaluation metric that is clinically aligned and
validated is required. As a solution, we present ExpertMedQA, a multispecialty
clinical benchmark comprised of open-ended, expert-level clinical questions,
and validated by a diverse group of medical professionals. By demanding an
in-depth understanding and critical appraisal of up-to-date clinical
literature, ExpertMedQA rigorously evaluates LLM performance. BooksMed
outperforms existing state-of-the-art models Med-PaLM 2, Almanac, and ChatGPT
in a variety of medical scenarios. Therefore, a framework that mimics human
cognitive stages could be a useful tool for providing reliable and
evidence-based responses to clinical inquiries.