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
10Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2319997164
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Jing-Jing Hu (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Innovation: 1 paper
- Org Design: 1 paper
Claim outcomes
- Governance And Regulation: 1 paper
- Market Structure: 1 paper
- Other: 1 paper
- Research Productivity: 1 paper
- Adoption Rate: 1 paper
- Employment: 1 paper
- Firm Revenue: 1 paper
- Organizational Efficiency: 1 paper
- Regulatory Compliance: 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 |
|---|---|---|---|
| NSF workshop urges a shift to clinic-aware algorithm–hardware co-design and sustained public investment in shared data, compute, and validation ecosystems to de-risk and speed medical AI commercialization. Without standardized benchmarks, continuous monitoring, and governance reforms, promising medical AI risks slow adoption, regulatory friction, and market concentration.arxiv | Jing-Jing Hu provider id |
2026-03-11 | 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.