Automatic diagnosis is a significant application of AI in healthcare, where
diagnoses are generated based on the symptom description of patients. Previous
works have approached this task directly by modeling the relationship between
the normalized symptoms and all possible diseases. However, in the clinical
diagnostic process, patients are initially consulted by a general practitioner
and, if necessary, referred to specialists in specific domains for a more
comprehensive evaluation. The final diagnosis often emerges from a
collaborative consultation among medical specialist groups. Recently, large
language models have shown impressive capabilities in natural language
understanding. In this study, we adopt tuning-free LLM-based agents as medical
practitioners and propose the Agent-derived Multi-Specialist Consultation
(AMSC) framework to model the diagnosis process in the real world by adaptively
fusing probability distributions of agents over potential diseases.
Experimental results demonstrate the superiority of our approach compared with
baselines. Notably, our approach requires significantly less parameter updating
and training time, enhancing efficiency and practical utility. Furthermore, we
delve into a novel perspective on the role of implicit symptoms within the
context of automatic diagnosis.

采用调谐自由的 LLM（大语言模型）代理作为医学从业者，我们提出了基于代理的多专科咨询（AMSC）框架，通过自适应地融合代理对潜在疾病的概率分布来模拟真实世界中的诊断过程。实验结果显示了我们的方法相比基线的优越性。值得注意的是，我们的方法需要极少的参数更新和训练时间，提高了效率和实用性。此外，我们深入探讨了在自动诊断背景下的隐性症状的新角度。

超越直接诊断：基于 LLM 的多专家代理咨询自动诊断

Beyond Direct Diagnosis: LLM-based Multi-Specialist Agent Consultation  for Automatic Diagnosis

In the last decade, the United States has lost more than 500,000 people from
an overdose involving prescription and illicit opioids
(this https URL) making it a national
public health emergency (USDHHS, 2017). To more effectively prevent
unintentional opioid overdoses, medical practitioners require robust and timely
tools that can effectively identify at-risk patients. Community-based social
media platforms such as Reddit allow self-disclosure for users to discuss
otherwise sensitive drug-related behaviors, often acting as indicators for
opioid use disorder. Towards this, we present a moderate size corpus of 2500
opioid-related posts from various subreddits spanning 6 different phases of
opioid use: Medical Use, Misuse, Addiction, Recovery, Relapse, Not Using. For
every post, we annotate span-level extractive explanations and crucially study
their role both in annotation quality and model development. We evaluate
several state-of-the-art models in a supervised, few-shot, or zero-shot
setting. Experimental results and error analysis show that identifying the
phases of opioid use disorder is highly contextual and challenging. However, we
find that using explanations during modeling leads to a significant boost in
classification accuracy demonstrating their beneficial role in a high-stakes
domain such as studying the opioid use disorder continuum. The dataset will be
made available for research on Github in the formal version.

为了更有效地预防意外的阿片类药物过量，医务人员需要强大且及时的工具来有效识别患者的风险，本研究通过 Reddit 这类社区平台中的自述信息，构建了一个中等规模的含有 2500 条与阿片类药物相关的帖子语料库，对这些帖子进行了分阶段的注释，并通过研究这些注释在模型开发和质量方面的作用，评估了几种最先进的模型，结果显示，在高风险领域如阿片类药物使用障碍研究中，使用解释可以显著提高分类准确度。