# CLARITY: Clinical Assistant for Routing, Inference, and Triage

arXiv: https://arxiv.org/abs/2510.02463 · HTML: https://mlsanity.com/paper/2510.02463/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 2 Oct 2025.

## Builds on (most cited first, 10 of 10)

- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning](https://mlsanity.com/paper/2311.10537/) (2023)
- [Large language models in healthcare and medical domain: A review](https://mlsanity.com/paper/2401.06775/) (2023)
- [A Survey of Large Language Models in Medicine: Progress, Application, and Challenge](https://mlsanity.com/paper/2311.05112/) (2023)
- [DISC-MedLLM: Bridging General Large Language Models and Real-World Medical Consultation](https://mlsanity.com/paper/2308.14346/) (2023)
- [CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis](https://mlsanity.com/paper/2407.13301/) (2024)
- [A preliminary study on evaluating Consultation Notes with Post-Editing](https://mlsanity.com/paper/2104.04402/) (2021)
- [NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named Entities](https://mlsanity.com/paper/2210.11913/) (2022)
- [Medical Dialogue: A Survey of Categories, Methods, Evaluation and Challenges](https://mlsanity.com/paper/2405.10630/) (2024)
- [Beyond Multiple-Choice Accuracy: Real-World Challenges of Implementing Large Language Models in Healthcare](https://mlsanity.com/paper/2410.18460/) (2024)

## Built on by (most cited first, 2 of 2)

- [MedConceal: A Benchmark for Clinical Hidden-Concern Reasoning Under Partial Observability](https://mlsanity.com/paper/2604.08788/) (2026)
- [Auditable Emergency Triage for Maternal and Newborn Care in India](https://mlsanity.com/paper/2609.09356/) (2026)

## Abstract (arXiv)

We present CLARITY (Clinical Assistant for Routing, Inference and Triage), an AI-driven platform designed to facilitate patient-to-specialist routing, clinical consultations, and severity assessment of patient conditions. Its hybrid architecture combines a Finite State Machine (FSM) for structured dialogue flows with collaborative agents that employ Large Language Model (LLM) to analyze symptoms and prioritize referrals to appropriate specialists. Built on a modular microservices framework, CLARITY ensures safe, efficient, and robust performance, flexible and readily scalable to meet the demands of existing workflows and IT solutions in healthcare. We report integration of our clinical assistant into a large-scale national interhospital platform, with more than 55,000 content-rich user dialogues completed within the two months of deployment, 2,500 of which were expert-annotated for subsequent validation. The validation results show that CLARITY surpasses human-level performance in terms of the first-attempt routing precision, naturally requiring up to 3 times shorter duration of the consultation than with a human.