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 dates unavailable. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
21666017
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Damodharan Narayanasamy (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Innovation: 1 paper
- Productivity: 1 paper
Claim outcomes
- Research Productivity: 1 paper
- Governance And Regulation: 1 paper
- Ai Safety And Ethics: 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 is accelerating pharmaceutical R&D in pilots—claims include cutting discovery timelines from years to weeks, slashing screening time and improving patient-selection accuracy—but the evidence is fragmented, often proprietary, and constrained by data bias, interpretability gaps and regulatory ambiguity.semantic_scholar | Damodharan Narayanasamy provider id |
Fetched 2026-03-17 | 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.