PRISM-Consult: A Panel-of-Experts Architecture for Clinician-Aligned Diagnosis
Not on the map. Why?
Abstract (arXiv)
We present PRISM-Consult, a clinician-aligned panel-of-experts architecture that extends the compact PRISM sequence model into a routed family of domain specialists. Episodes are tokenized as structured clinical events; a light-weight router reads the first few tokens and dispatches to specialist models (Cardiac-Vascular, Pulmonary, Gastro-Oesophageal, Musculoskeletal, Psychogenic). Each specialist inherits PRISM's small transformer backbone and token template, enabling parameter efficiency and interpretability. This initial study evaluates a scoped panel of five specialist families defined by high-impact ED diagnostic groups. On real-world Emergency Department cohorts, specialists exhibit smooth convergence with low development perplexities across domains, while the router achieves high routing quality and large compute savings versus consult-all under a safety-first policy. We detail the data methodology (initial vs. conclusive ICD-9 families), routing thresholds and calibration, and report per-domain results to avoid dominance by common events. The framework provides a practical path to safe, auditable, and low-latency consult at scale, and we outline validation steps-external/temporal replication, asymmetric life-threat thresholds, and multi-label arbitration-to meet prospective clinical deployment standards.
Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 15 of its 38 references that are in the map. First posted on arXiv 1 Oct 2025.
Builds on
15 of 38 references.
References · 15
- 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,044
- 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,942
- 2022Large Language Models Encode Clinical Knowledgecited by 1,817
- 2020GShard: Scaling Giant Models with Conditional Computation and Automatic Shardingcited by 1,618
- 2024RouteLLM: Learning to Route LLMs with Preference Datacited by 652
- 2022Mixture-of-Experts with Expert Choice Routingcited by 619
- 2022BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Miningcited by 536
- 2019ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmissioncited by 490
- 2024Mixture-of-Agents Enhances Large Language Model Capabilitiescited by 426
- 2022GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Recordscited by 238
- 2024Zero Shot Health Trajectory Prediction Using Transformercited by 76
- 2024Eagle: Efficient Training-Free Router for Multi-LLM Inferencecited by 35
Built on by
Not cited yet.