ArticleNPJ digital medicine2025
The FAIIR conversational AI agent assistant for youth mental health service provision.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- An LLM chatbot to facilitate primary-to-specialist care transitions: a randomized controlled trial.Nature medicine · 2026Trial
- A five-phase evaluation framework for diagnostic and predictive medical artificial intelligence.NPJ digital medicine · 2026Article
- Technology-mediated screening interviews for youth mental health: Content validation, randomized controlled trial, and expert evaluation.PLOS digital health · 2026Article
- Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Frontline crisis support plays a critical role in youth mental health services, where Crisis Responders (CRs) engage in conversations and assign issue tags to guide interventions. To enhance this process, we introduce FAIIR (Frontline Assistant: Issue Identification and Recommendation), an ensemble of domain-adapted transformer models trained on 780,000 conversations. FAIIR aims to reduce CR's cognitive burden, enhance issue identification accuracy, and streamline post-conversation administrative tasks. Evaluated on retrospective data, FAIIR achieves an average AUC ROC of 94%, an average F1-score of 64%, and an average recall score of 81%. During the silent testing phase, its performance remained robust, with less than a 2% drop in all metrics. CRs exhibited 90.9% agreement with its predictions, and expert agreement with FAIIR exceeded their agreement with original labels. These findings highlight FAIIR's potential to assist CRs in prioritizing urgent cases and ensuring appropriate resource allocation in crisis interventions.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.