Official Repo for the paper "Large Language Models are Clinical Reasoners: Reasoning-Aware Diagnosis Framework with Prompt-Generated Rationales".
Paper link: https://arxiv.org/abs/2312.07399
This repo contains two few-shot clinical chain-of-thought exemplars, each contains:
patient description + clinical CoT rationales + final diagnosis
The CoT rationales here are created by humans and machines (2 licensed radiologists and GPT-4 work together).
These shots are used to prompt the LLMs to generate clinical rationales that link the provided patient information (e.g., EHR) to the final diagnosis of Alzheimer's disease (AD, MCI, or NC).
Specifically, these are used in our Module I: Clinical Rationalization and Module II-1: Few-shot CoT Reasoning.
Researchers can utilize our expert-provided exemplars to guide the CoT reasoning in AD diagnosis with LLMs.
This can serve as a strong baseline for comparison.
The use of these exemplars should be only for educational purposes.
If you applied our exemplars in your work, please use the following BibTeX to cite our paper
(instead of citing the ArXiv one):
@inproceedings{kwon2024large,
title={Large Language Models Are Clinical Reasoners: Reasoning-Aware Diagnosis Framework with Prompt-Generated Rationales},
author={Kwon, Taeyoon and Ong, Kai Tzu-iunn and Kang, Dongjin and Moon, Seungjun and Lee, Jeong Ryong and Hwang, Dosik and Sohn, Beomseok and Sim, Yongsik and Lee, Dongha and Yeo, Jinyoung},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={38},
number={16},
pages={18417--18425},
year={2024}
}