An accurate differential diagnosis (DDx) is a cornerstone of medical care,
often reached through an iterative process of interpretation that combines
clinical history, physical examination, investigations and procedures.
Interactive interfaces powered by Large Language Models (LLMs) present new
opportunities to both assist and automate aspects of this process. In this
study, we introduce an LLM optimized for diagnostic reasoning, and evaluate its
ability to generate a DDx alone or as an aid to clinicians. 20 clinicians
evaluated 302 challenging, real-world medical cases sourced from the New
England Journal of Medicine (NEJM) case reports. Each case report was read by
two clinicians, who were randomized to one of two assistive conditions: either
assistance from search engines and standard medical resources, or LLM
assistance in addition to these tools. All clinicians provided a baseline,
unassisted DDx prior to using the respective assistive tools. Our LLM for DDx
exhibited standalone performance that exceeded that of unassisted clinicians
(top-10 accuracy 59.1% vs 33.6%, [p = 0.04]). Comparing the two assisted study
arms, the DDx quality score was higher for clinicians assisted by our LLM
(top-10 accuracy 51.7%) compared to clinicians without its assistance (36.1%)
(McNemar's Test: 45.7, p < 0.01) and clinicians with search (44.4%) (4.75, p =
0.03). Further, clinicians assisted by our LLM arrived at more comprehensive
differential lists than those without its assistance. Our study suggests that
our LLM for DDx has potential to improve clinicians' diagnostic reasoning and
accuracy in challenging cases, meriting further real-world evaluation for its
ability to empower physicians and widen patients' access to specialist-level
expertise.

本研究介绍了一种为诊断推理进行优化的大型语言模型 (LLM)，并评估其生成独立诊断推理 (DDx) 或为临床医生提供辅助的能力。结果显示，在挑战性真实医学案例中，使用 LLM 辅助的临床医生在 DDx 质量和全面性方面表现优于未使用该辅助工具的医生，证明 LLM 为 DDx 提供了潜力，有望改善医生在复杂病例中的诊断推理和准确性。

借助大型语言模型实现准确的鉴别诊断

Towards Accurate Differential Diagnosis with Large Language Models

While search is the predominant method of accessing information, formulating
effective queries remains a challenging task, especially for situations where
the users are not familiar with a domain, or searching for documents in other
languages, or looking for complex information such as events, which are not
easily expressible as queries. Providing example documents or passages of
interest, might be easier for a user, however, such query-by-example scenarios
are prone to concept drift, and are highly sensitive to the query generation
method. This demo illustrates complementary approaches of using LLMs
interactively, assisting and enabling the user to provide edits and feedback at
all stages of the query formulation process. The proposed Query Generation
Assistant is a novel search interface which supports automatic and interactive
query generation over a mono-linguial or multi-lingual document collection.
Specifically, the proposed assistive interface enables the users to refine the
queries generated by different LLMs, to provide feedback on the retrieved
documents or passages, and is able to incorporate the users' feedback as
prompts to generate more effective queries. The proposed interface is a
valuable experimental tool for exploring fine-tuning and prompting of LLMs for
query generation to qualitatively evaluate the effectiveness of retrieval and
ranking models, and for conducting Human-in-the-Loop (HITL) experiments for
complex search tasks where users struggle to formulate queries without such
assistance.

该研究论文介绍了一种新的搜索界面，支持对单语或多语言文档集合进行自动生成查询，并通过用户反馈来优化查询，从而解决用户在查询表达方面的困难。