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.
@article{arxiv.2310.11266,
title = {Emulating Human Cognitive Processes for Expert-Level Medical Question-Answering with Large Language Models},
author = {Khushboo Verma and Marina Moore and Stephanie Wottrich and Karla Robles López and Nishant Aggarwal and Zeel Bhatt and Aagamjit Singh and Bradford Unroe and Salah Basheer and Nitish Sachdeva and Prinka Arora and Harmanjeet Kaur and Tanupreet Kaur and Tevon Hood and Anahi Marquez and Tushar Varshney and Nanfu Deng and Azaan Ramani and Pawanraj Ishwara and Maimoona Saeed and Tatiana López Velarde Peña and Bryan Barksdale and Sushovan Guha and Satwant Kumar},
journal= {arXiv preprint arXiv:2310.11266},
year = {2023}
}