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
23Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2025. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2334001434
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Kevin Schulman (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Productivity: 1 paper
Claim outcomes
- Organizational Efficiency: 1 paper
- Other: 1 paper
- Consumer Welfare: 1 paper
- Decision Quality: 1 paper
- Error Rate: 1 paper
- Governance And Regulation: 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 machine-learning alert cut repeat complete blood counts by 15% in a randomized pilot across two hospitals, lowering unnecessary inpatient testing without measurable harms; SmartAlert users averaged 1.54 vs 1.82 CBCs within 52 hours (p < 0.01).arxiv | Kevin Schulman provider id |
2025-12-04 | 1 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 1 cumulative citations. This is a coverage summary, not an author score or h-index.