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
7Unique collaborators
0/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
No provider ID is stored.
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Xin-Xin Fang (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Productivity: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Organizational Efficiency: 1 paper
- Task Completion Time: 1 paper
- Decision Quality: 1 paper
- Other: 1 paper
- Output Quality: 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 |
|---|---|---|---|
| A gaze-guided vision transformer paired with an ontology-constrained vision–language model speeds retinal reads and slashes documentation time: combined Co-Annotator guidance increased correct diagnoses per minute by 40% and reduced comment editing time by 67% without reducing diagnostic accuracy, while substantially expanding documented biomarkers.arxiv | Xin-Xin Fang unresolved |
2026-08-31 | Missing, not zero |
Citation observation summary
Semantic Scholar supplied counts for 0 of 1 papers in this view; 1 are missing.