ReviewJournal of traumatic stress2026
Artificial intelligence in traumatic stress treatment: The TRUST framework for ethical development, clinical applications, and research advancement.
Review in Journal of traumatic stress, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Posttraumatic stress disorder (PTSD) and depression are common diagnoses following traumatic events, with several available evidence-based interventions to reduce symptomology. However, trauma populations face significant access barriers that limit their adoption and reach. Artificial intelligence (AI) technologies, such as large language models (LLMs), have the potential to enhance access, cost-efficiency, delivery, and quality of traumatic stress interventions. Their application to traumatic stress treatment and research is understudied and requires responsible and ethical development, evaluation, and monitoring to maintain service quality and delivery for trauma survivors and their providers. We present considerations for the responsible use of AI tools to ethically shape trauma-focused treatment and research, with future use cases to highlight these critical considerations. A multidisciplinary approach to trauma-based LLM development that integrates feedback and evaluation from trauma experts, clinicians, and trauma survivors and prioritizes the quality, safety, effectiveness, and equity of trauma-focused care is essential. We propose a framework, TRUST, that is informed by evidence from adjacent mental health AI applications and emerging research on digital trauma interventions, while acknowledging the limited trauma-specific AI trial data. These topics were presented at the 41st Annual Meeting of the International Society for Traumatic Stress Studies via a panel of experts in AI technologies to support trauma-focused interventions.
Identifiers
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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.