Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review.
How this is built →
1Distinct papers
2Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2455551741
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Talitha Nabilah (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Productivity: 1 paper
Claim outcomes
- Ai Safety And Ethics: 1 paper
- Organizational Efficiency: 1 paper
- Consumer Welfare: 1 paper
- Decision Quality: 1 paper
- Error Rate: 1 paper
- Governance And Regulation: 1 paper
- Market Structure: 1 paper
- Other: 1 paper
- Task Allocation: 1 paper
Papers in the Semantic Scholar view
Latest stored Semantic Scholar author observations only. Citation counts below are from the same provider and are not combined with other services.
Scroll the table horizontally to see every column.
| Paper | Author evidence | Date | Provider citations |
|---|---|---|---|
| AI chatbots can extend adolescent mental-health reach and reduce marginal screening costs, but their promise hinges on better adolescent-language NLP and automatic clinical escalation; without robust detection of crisis language and seamless referral to nurses, misclassifications create clinical and legal risks that could negate economic benefits.openalex | Talitha Nabilah provider id |
2026-08-02 | 0 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 0 cumulative citations. This is a coverage summary, not an author score or h-index.