# Emulating Human Cognitive Processes for Expert-Level Medical Question-Answering with Large Language Models

Source: [Emulating Human Cognitive Processes for Expert-Level Medical Question-Answering with Large Language Models](<https://www.sota2.com/research/paper/68fa70ee1e209131cea4e1db>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:53:50.252Z

Authors: Khushboo Verma, Marina Moore, Stephanie Wottrich, Karla Robles L\\'opez, 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 L\\'opez Velarde Pe\\~na, Bryan Barksdale, Sushovan Guha, Satwant Kumar

Published: 2023-10-17T13:39:26.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2310.11266>)

[PDF](<https://arxiv.org/pdf/2310.11266.pdf>)