Observational studyNature medicine2024
Closing the accessibility gap to mental health treatment with a personalized self-referral chatbot.
Observational study in Nature medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 2 of them syntheses that pooled it.
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
51 citing papers in PubMed, 2 syntheses or guidelines pooled it, 83 citations in OpenAlex.
- Digital front door technologies to pre-assess people before assessment for NHS Talking Therapies [HTE10055]: early value assessment.Health technology assessment (Winchester, England) · 2026Pooled it
- Generative AI Mental Health Chatbots as Therapeutic Tools: Systematic Review and Meta-Analysis of Their Role in Reducing Mental Health Issues.Journal of medical Internet research · 2025Pooled it
- A pragmatic randomized controlled trial of self-directed online writing interventions for posttraumatic stress symptoms in a real-world digital setting.European journal of psychotraumatology · 2026Trial
- Comparison of an AI Chatbot With a Nurse Hotline in Reducing Anxiety and Depression Levels in the General Population: Pilot Randomized Controlled Trial.JMIR human factors · 2025Trial
- An Evidence-Based AI Virtual Assistant for Young People With Attention Deficit Hyperactivity Disorder: Co-Design and Prototype Development.JMIR formative research · 2026Article
- A clinically validated framework for auditing AI chatbot behavior in mental health interactions.Nature medicine · 2026Article
- Artificial Intelligence in Child and Adolescent Psychiatry: A Narrative Review of Recent Clinical Applications and Ethical Considerations.Current psychiatry reports · 2026Review
- Agentic AI in global health equity for high altitude populations.NPJ digital medicine · 2026Review
- The eHealth literacy and its sociodemographic determinants among adult inpatients in Web 3.0 context: a cross-sectional study.BMC public health · 2026Article
- Comprehensive Model for Mental health Access and service use (CoMMA): A process model for technology-enhanced mental healthcare.Internet interventions · 2026Review
- Mapping the role of artificial intelligence in health-related stigma: a scoping review.NPJ digital medicine · 2026Article
- Safety Mechanisms and Risk Mitigation in Generative AI Mental Health Chatbots: A Systematic Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
- Healthcare experiences of transgender and gender-diverse people in the UK: a scoping review.BMJ open · 2026Article
- Can Medical Chatbots Trigger Disinhibition and Encourage Health Information Disclosure?Healthcare (Basel, Switzerland) · 2026Article
- Blocking the Reflection: Milestones and Hurdles for Digital Twins in Mental Health.Pharmacopsychiatry · 2026Review
- Total product lifecycle regulatory considerations and recommendations for generative AI-enabled medical devices.European heart journal. Digital health · 2026Article
- Retrieval‑augmented large language models for depression screening and suicide risk stratification.BMC psychiatry · 2026Article
- Mass Media Narratives of Psychiatric Adverse Events Associated With Generative AI Chatbots: Rapid Scoping Review.JMIR mental health · 2026Article
- LGBTQ+ experiences of accessing NHS adult mental health services during COVID-19 in an area of North West England: a qualitative interview study.BMC health services research · 2026Article
- Scaling medical AI across clinical contexts.Nature medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inequality in treatment access is a pressing issue in most healthcare systems across many medical disciplines. In mental healthcare, reduced treatment access for minorities is ubiquitous but remedies are sparse. Here we demonstrate that digital tools can reduce the accessibility gap by addressing several key barriers. In a multisite observational study of 129,400 patients within England's NHS services, we evaluated the impact of a personalized artificial intelligence-enabled self-referral chatbot on patient referral volume and diversity in ethnicity, gender and sexual orientation. We found that services that used this digital solution identified substantially increased referrals (15% increase versus 6% increase in control services). Critically, this increase was particularly pronounced in minorities, such as nonbinary (179% increase) and ethnic minority individuals (29% increase). Using natural language processing to analyze qualitative feedback from 42,332 individuals, we found that the chatbot's human-free nature and the patients' self-realization of their need for treatment were potential drivers for the observed improvement in the diversity of access. This provides strong evidence that digital tools may help overcome the pervasive inequality in mental healthcare.
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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.