AInsight: Augmenting Expert Decision-Making with On-the-Fly Insights Grounded in Historical Data
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913938952683520 |
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| author | Abolnejadian, Mohammad Amirshahi, Shakiba Brehmer, Matthew Crisan, Anamaria |
| author_facet | Abolnejadian, Mohammad Amirshahi, Shakiba Brehmer, Matthew Crisan, Anamaria |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_09100 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AInsight: Augmenting Expert Decision-Making with On-the-Fly Insights Grounded in Historical Data Abolnejadian, Mohammad Amirshahi, Shakiba Brehmer, Matthew Crisan, Anamaria Human-Computer Interaction Artificial Intelligence Computation and Language H.5.0 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. |
| title | AInsight: Augmenting Expert Decision-Making with On-the-Fly Insights Grounded in Historical Data |
| topic | Human-Computer Interaction Artificial Intelligence Computation and Language H.5.0 |
| url | https://arxiv.org/abs/2507.09100 |