Digital Mental Health Research Intelligence
PubMed corpus · 2024–2026 · Click any chart to browse papers
5,703 papers
100+ countries
277 companies
Updated Apr 2026
Total Papers
5,703
2024–2026
2024
1,838
papers published
2025
3,025
+65% vs 2024
2026 (YTD)
840
through Apr
Countries
100+
represented
Companies
277
industry involvement
Key finding: Publications grew 65% from 2024 to 2025 (1,838 → 3,025 papers). LLM/Generative AI research now covers 521 papers across the corpus. The USA, China, and UK remain the top publishing countries.
Publications Over Timeclick to filter
Publication Typesclick to filter
Top Journalsclick to filter
Top Keywordsclick to filter
Depression and anxiety dominate target conditions. LLM-based approaches are the fastest-growing AI method. Trust and therapeutic alliance are emerging as distinct outcome threads beyond efficacy. Click any bar to explore those papers.
Depression
2,440
most studied condition
Machine Learning
3,403
top AI method
LLM / GenAI
521
fastest growing
Youth-focused
1,697
youth_or_adolescent
Target Conditionsclick to filter
AI Methodsclick to filter
Product Typesclick to filter
Clinical Functionclick to filter
Outcome Focusclick to filter
Populationclick to filter
Study Designclick to filter
Theme Growth 2024 → 2025 → 2026click to filter
Evidence maturity shows how much high-quality research (RCTs + systematic reviews) backs each theme. Depression and Anxiety have the deepest evidence bases. LLM/Generative AI is growing fast but remains mostly observational. Click any bar or cell to filter papers.
Themes Tracked
45
across 9 dimensions
Most RCTs
1,249
randomized_or_trial
Evidence-light
LLM/GenAI
growing fast, few RCTs
Highest Maturity
Efficacy
score 2,738
Theme Maturity — Evidence Breakdownclick to filter
Evidence Matrixclick a cell or theme name
Maturity Overview
| Tag | Dimension | Papers | RCTs | Reviews | Observational | Maturity | Label |
|---|
John Torous (Harvard/JMIR) leads with 38 papers. The US, UK, and China dominate by volume. Click any author or country bar to see their papers.
Top Authorsclick to filter
Papers by Countryclick to filter
Top Institutionsby paper count
Industry engagement: 277 companies involved. Fit Minded leads with 18 papers. Many have strong academic collaboration rates (>70% co-authored with academia). Click any company to see their papers.
Companies Detected
277
in corpus
Company-linked Papers
403
~7.1% of corpus
Top Company
Fit Minded
18 papers
Top Companies by Papersclick to filter
Academic Collaboration Rate
Company Details
| # | Company | Papers | Academic Collabs | Rate |
|---|
Intelligence Digest highlights the highest-impact papers, fastest-growing research themes, and most influential researchers — a quick briefing on where the field is heading.
Highest-Impact Papersranked by composite score (citations × theme breadth)
| Title | Journal | Year | Themes | Cited | Score |
|---|
Fastest-Growing Themes2024 → 2025 growth rate
Growth Tableclick a row to browse papers
| Theme | Dimension | 2024 | 2025 | Growth |
|---|
Researcher Spotlightsranked by composite influence score · click to see papers
| # | Researcher | Papers | Total Citations | Theme Breadth | Industry | Score |
|---|
Filter Papers
| Title | Journal | Authors | Year | Type | Tags | Cited |
|---|
Builder View — actionable intelligence for founders building AI digital mental health products. Risk radar, design patterns, competitive landscape, and market gap analysis derived from 5,587 LLM-classified papers.
Risk Entries
—
condition × intervention × risk
Design Patterns
—
papers with frameworks
Companies Tracked
—
industry players
Market Gaps
—
condition × intervention combos
Risk Radar — Condition × Risk Typeclick cell to browse papers
Paper counts across conditions and risk types. (green) = papers with mitigations (design patterns or guidelines). Green-tinted cells = >30% coverage.
| Condition |
|---|
Design Patterns by Product Typeclick to filter
Maturity Stage Breakdown
Top Design Patterns & Frameworksby citations — papers that provide reusable frameworks, guidelines, or tools
| Title | Product Type | Condition | Maturity | Relevance | Cited |
|---|
Top Companies by Papersclick to filter
Company Details
| # | Company | Papers | Cites | Conditions | Maturity |
|---|
Reading List — queue papers for deep analysis, track progress, and annotate applicability to Viaduco. Only available when running locally via
python -m research_analyst serve.
Your Reading List
How This Corpus Was Built
Step 1 — Collection
Source: PubMed E-utilities API (peer-reviewed literature, Jan 2024 – present).
7 topic queries covering: digital mental health, mental health chatbots, AI interventions, evaluation of AI tools, regulatory/ethics frameworks, implementation science, and UX/design.
Papers appearing in multiple queries are deduplicated and merged. Exclusion filters remove oncology, cardiovascular, diabetes, and other off-topic domains.
7 topic queries covering: digital mental health, mental health chatbots, AI interventions, evaluation of AI tools, regulatory/ethics frameworks, implementation science, and UX/design.
Papers appearing in multiple queries are deduplicated and merged. Exclusion filters remove oncology, cardiovascular, diabetes, and other off-topic domains.
Step 2 — Enrichment
Each paper is enriched from three sources:
Unpaywall — open-access status and PDF links (via DOI lookup).
OpenAlex — citation counts (batch API, updated per run).
LLM Classification (Claude Haiku) — each abstract is sent to Claude to classify across 8 dimensions: actionability, startup relevance, maturity stage, clinical function, outcome direction, engagement model, target user, and sample size. Tags are stored and reused across runs.
Unpaywall — open-access status and PDF links (via DOI lookup).
OpenAlex — citation counts (batch API, updated per run).
LLM Classification (Claude Haiku) — each abstract is sent to Claude to classify across 8 dimensions: actionability, startup relevance, maturity stage, clinical function, outcome direction, engagement model, target user, and sample size. Tags are stored and reused across runs.
Step 3 — Keyword Tagging
Title, abstract, keywords, and MeSH terms are matched against a rule-based taxonomy with 9 keyword dimensions and ~45 tags. This covers product type, AI method, condition, population, outcome focus, paper orientation, safety/ethics, product relevance, and study design.
Step 4 — Analysis & Visualization
Keyword and LLM tags are combined to produce: theme trends, evidence maturity scores, risk registries, design pattern catalogs, competitive intelligence, and gap analysis. Geographic and institutional data is extracted from author affiliations. Company involvement is detected via affiliation pattern matching.
Tag Dictionary
All dimensions and tags used to classify papers. Keyword dimensions are matched from text; LLM dimensions are classified by AI from the abstract.
Product Type (keyword)
What form factor does the intervention take?
chatbot mobile_app digital_therapeutic web_platform wearable
AI Method (keyword)
What AI/ML technique is used?
llm_or_generative_ai machine_learning
Condition (keyword)
Which mental health condition is studied?
depression anxiety suicide_or_self_harm psychosis ptsd bipolar_disorder serious_mental_illness
Population (keyword)
Who is the target population?
youth_or_adolescent adult older_adult student veteran caregiver
Outcome Focus (keyword)
What outcome is the paper measuring or discussing?
efficacy engagement trust safety adherence therapeutic_alliance
Paper Orientation (keyword)
What is the paper's stance or framing?
advantage_or_benefit barrier_or_challenge risk_or_harm adoption_or_uptake
Safety & Ethics (keyword)
What safety or ethical concern is addressed?
clinical_safety data_privacy algorithmic_bias informed_consent
Product Relevance (keyword)
Does the paper provide actionable product or business guidance?
design_pattern evaluation_framework regulatory_pathway business_model
Study Design (keyword)
What type of study was conducted?
randomized_or_trial review_or_synthesis protocol feasibility_or_pilot validation_or_prediction
Actionability (LLM)
What does this paper offer a builder?
provides_framework reports_findings_only proposes_guidelines presents_tool_or_system
Startup Relevance (LLM)
How is this useful to someone building a product?
build_guidance market_signal risk_to_mitigate competitive_landscape
Maturity Stage (LLM)
What development stage does the work describe?
concept prototype clinical_validation real_world_deployment
Clinical Function (LLM)
What clinical purpose does the tool serve?
screening assessment monitoring triage treatment prevention self_management clinical_decision_support
Outcome Direction (LLM)
What were the reported outcomes?
positive negative mixed not_reported
Engagement Model (LLM)
How does the user interact with the tool?
self_guided therapist_guided blended fully_automated
Target User (LLM)
Who is the intended user of the tool?
patient clinician caregiver researcher
Sample Size (LLM)
How large is the study's sample?
tiny (N<30) small (30-100) medium (100-500) large (N>500)
Produced by
This dashboard was researched and produced by the Viaduco team. Viaduco is building a digital mental health app. © 2026 Viaduco. All rights reserved. Data sourced from PubMed. For research and informational purposes only.