TRIAGE: Dialectical Reasoning for Explainable Risk Prediction on Irregularly Sampled Medical Time Series with LLMs
arXiv:2606.09030v1 Announce Type: new Abstract: Clinical early warning systems built on electronic health records, in which clinical observations are recorded as irregularly sampled medical time series (ISMTS), must deliver both calibrated risk scores for patient triage and interpretable rationales that clinicians can verify. Large Language Models (LLMs) have been explored for this task, yet they collapse graded clinical risk into overconfident binary predictions. This risk polarization undermines both calibration and cross-patient comparability. To address this, we propose TRIAGE, a framework
The proliferation of LLMs creates a need to understand their limitations, especially in critical applications like medical prognosis, driving research into explainability and calibration.
This work directly addresses a significant deficiency in LLMs for high-stakes medical decisions, offering a more robust and verifiable approach to AI-driven risk assessment.
The development of frameworks like TRIAGE begins to shift LLM application in healthcare from uncalibrated predictions to more reliable, explainable, and context-aware systems.
- · Clinical AI developers
- · Healthcare providers
- · Patients
- · AI explainability researchers
- · Uncalibrated LLM-based clinical systems
- · Black-box AI solutions in healthcare
Improved accuracy and trust in AI-driven early warning systems within hospitals.
Accelerated adoption of LLM-based tools in critical healthcare workflows due to increased reliability and explainability.
Potential for new regulatory frameworks specifically addressing the calibration and interpretability of AI in medical diagnostics and prognosis.
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Read at arXiv cs.LG