AInsight：利用基于历史数据的即时洞察增强专家决策
AInsight: Augmenting Expert Decision-Making with On-the-Fly Insights Grounded in Historical Data
AI总结：
本文提出AInsight系统，通过基于检索的LLM智能体流水线实时监听对话并从历史数据中生成洞察，以医患互动为例验证了其在增强专家实时决策中的有效性与挑战。
摘要：
在决策对话中，专家必须在交谈时处理复杂的选择并做出即时决定。尽管通常存在大量历史数据，但这些场景的实时性使得决策者无法审查并利用相关信息。这引发了一个有趣的问题：如果专家能通过从过去数据中得出的洞察，在实时决策中利用相关的过去数据会怎样？为了探索这一点，我们以医患互动为示例用例，实现了一个对话用户界面。我们的系统持续监听对话，识别患者问题和医生建议的解决方案，并从嵌入的数据集中检索相关数据，利用围绕基于检索的大语言模型（LLM）智能体构建的流水线生成简洁洞察。我们通过将 Health Canada 数据集嵌入向量数据库，并使用样本医患对话进行模拟研究来评估原型，结果显示了有效性但也存在挑战，为我们下一步的工作设定了方向。
英文摘要：
In decision-making conversations, experts must navigate complex choices and make on-the-spot decisions while engaged in conversation. Although extensive historical data often exists, the real-time nature of these scenarios makes it infeasible for decision-makers to review and leverage relevant information. This raises an interesting question: What if experts could utilize relevant past data in real-time decision-making through insights derived from past data? To explore this, we implemented a conversational user interface, taking doctor-patient interactions as an example use case. Our system continuously listens to the conversation, identifies patient problems and doctor-suggested solutions, and retrieves related data from an embedded dataset, generating concise insights using a pipeline built around a retrieval-based Large Language Model (LLM) agent. We evaluated the prototype by embedding Health Canada datasets into a vector database and conducting simulated studies using sample doctor-patient dialogues, showing effectiveness but also challenges, setting directions for the next steps of our work.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
