With appropriate data selection and training techniques, Large Language
Models (LLMs) have demonstrated exceptional success in various medical
examinations and multiple-choice questions. However, the application of LLMs in
medical dialogue generation-a task more closely aligned with actual medical
practice-has been less explored. This gap is attributed to the insufficient
medical knowledge of LLMs, which leads to inaccuracies and hallucinated
information in the generated medical responses. In this work, we introduce the
Medical dialogue with Knowledge enhancement and clinical Pathway encoding
(MedKP) framework, which integrates an external knowledge enhancement module
through a medical knowledge graph and an internal clinical pathway encoding via
medical entities and physician actions. Evaluated with comprehensive metrics,
our experiments on two large-scale, real-world online medical consultation
datasets (MedDG and KaMed) demonstrate that MedKP surpasses multiple baselines
and mitigates the incidence of hallucinations, achieving a new
state-of-the-art. Extensive ablation studies further reveal the effectiveness
of each component of MedKP. This enhancement advances the development of
reliable, automated medical consultation responses using LLMs, thereby
broadening the potential accessibility of precise and real-time medical
assistance.

通过结合医学知识图谱和内部临床路径编码，本研究提出了医学对话增强与临床路径编码（MedKP）框架，通过综合评估指标在两个大规模实际在线医学咨询数据集上的实验证明，MedKP 超过了多个基准系统，减少了产生幻觉的情况，达到了新的最先进水平，进一步的剔除研究揭示了 MedKP 每个组件的有效性，促进了可靠自动化的医学咨询响应的发展，从而拓宽了精确实时医学辅助的潜在可及性。

MedKP: 医疗对话中的知识增强和临床路径编码

MedKP: Medical Dialogue with Knowledge Enhancement and Clinical Pathway  Encoding

The copilot framework, which aims to enhance and tailor large language models
(LLMs) for specific complex tasks without requiring fine-tuning, is gaining
increasing attention from the community. In this paper, we introduce the
construction of a Healthcare Copilot designed for medical consultation. The
proposed Healthcare Copilot comprises three main components: 1) the Dialogue
component, responsible for effective and safe patient interactions; 2) the
Memory component, storing both current conversation data and historical patient
information; and 3) the Processing component, summarizing the entire dialogue
and generating reports. To evaluate the proposed Healthcare Copilot, we
implement an auto-evaluation scheme using ChatGPT for two roles: as a virtual
patient engaging in dialogue with the copilot, and as an evaluator to assess
the quality of the dialogue. Extensive results demonstrate that the proposed
Healthcare Copilot significantly enhances the capabilities of general LLMs for
medical consultations in terms of inquiry capability, conversational fluency,
response accuracy, and safety. Furthermore, we conduct ablation studies to
highlight the contribution of each individual module in the Healthcare Copilot.
Code will be made publicly available on GitHub.

提出的医疗协作助手通过对话、记忆和处理三个组件的结合，提高了大型语言模型在医疗咨询中的查询能力、对话流畅度、回答准确性和安全性。

医疗协助导航系统：调动通用 LLM 在医疗咨询中的力量

Healthcare Copilot: Eliciting the Power of General LLMs for Medical  Consultation

Most medical dialogue systems assume that patients have clear goals (medicine
querying, surgical operation querying, etc.) before medical consultation.
However, in many real scenarios, due to the lack of medical knowledge, it is
usually difficult for patients to determine clear goals with all necessary
slots. In this paper, we identify this challenge as how to construct medical
consultation dialogue systems to help patients clarify their goals. To mitigate
this challenge, we propose a novel task and create a human-to-human mixed-type
medical consultation dialogue corpus, termed MidMed, covering five dialogue
types: task-oriented dialogue for diagnosis, recommendation, knowledge-grounded
dialogue, QA, and chitchat. MidMed covers four departments
(otorhinolaryngology, ophthalmology, skin, and digestive system), with 8,175
dialogues. Furthermore, we build baselines on MidMed and propose an
instruction-guiding medical dialogue generation framework, termed InsMed, to
address this task. Experimental results show the effectiveness of InsMed.

本文提出了如何构建医疗咨询对话系统来帮助患者澄清他们的目标，介绍了一个混合式医疗咨询对话语料库 MidMed 及其基线，提出了一种指导型医学对话生成框架 InsMed，并证明了其有效性。

MidMed：针对医疗咨询的混合对话技术

MidMed: Towards Mixed-Type Dialogues for Medical Consultation

In this paper, we present HuatuoGPT, a large language model (LLM) for medical
consultation. The core recipe of HuatuoGPT is to leverage both
\textit{distilled data from ChatGPT} and \textit{real-world data from doctors}
in the supervised fine-tuned stage. The responses of ChatGPT are usually
detailed, well-presented and informative while it cannot perform like a doctor
in many aspects, e.g. for integrative diagnosis. We argue that real-world data
from doctors would be complementary to distilled data in the sense the former
could tame a distilled language model to perform like doctors. To better
leverage the strengths of both data, we train a reward model to align the
language model with the merits that both data bring, following an RLAIF
(reinforced learning from AI feedback) fashion. To evaluate and benchmark the
models, we propose a comprehensive evaluation scheme (including automatic and
manual metrics). Experimental results demonstrate that HuatuoGPT achieves
state-of-the-art results in performing medical consultation among open-source
LLMs in GPT-4 evaluation, human evaluation, and medical benchmark datasets. It
is worth noting that by using additional real-world data and RLAIF, the
distilled language model (i.e., HuatuoGPT) outperforms its teacher model
ChatGPT in most cases. Our code, data, and models are publicly available at
\url{this https URL}. The online demo is
available at \url{this https URL}.

HuatuoGPT 是一款用于医学咨询的大型语言模型，通过结合 ChatGPT 的蒸馏数据和医生的实际数据进行监督精调，并使用增强学习模型以更好地利用两种数据的优势，表现出出色的实验效果。