arXiv:2503.08292v5 Announce Type: replace-cross Abstract: Outpatient referral (OR) is a core clinical workflow that assigns patients to hospital departments under incomplete and evolving information, yet it is commonly simplified as a static classification problem despite being inherently interactive in practice. In this work, we study outpatient referral as a dynamic process driven by information acquisition and uncertainty reduction. We analyze both static scenarios based on fixed patient information and dynamic scenarios involving multi-turn dialogue, to test whether large language models (
The rapid advancement of large language models makes their application in complex human-centric tasks like medical triage a timely and critical area of study.
This study is important because it evaluates the capacity of LLMs to handle dynamic, interactive clinical reasoning, which has significant implications for healthcare efficiency and patient outcomes.
The focus shifts from LLMs as static classifiers to dynamic agents capable of interactive information acquisition for complex decision-making in real-world scenarios.
- · Healthcare AI developers
- · Patients in regions with healthcare access issues
- · Digital health platforms
- · Traditional healthcare administrative processes
- · Medical professionals performing routine triage
Healthcare systems may begin piloting LLM-driven triage systems to improve efficiency and reduce wait times.
Reduced physician burnout from administrative tasks but increased demand for training and oversight of AI systems.
The legal and ethical frameworks around AI responsibility in medical decision-making will need to significantly evolve, potentially leading to new regulatory bodies.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI