3 Key Takeaways
Plain English Summary- 01Core Finding
Scientific journals and professional organizations agree that authors should disclose AI assistance.
- 02Study Evidence
Tested in a observational cohort study at the real-world cohort stage.
- 03Practical Meaning
Observational cohort findings reveal real-world links, though lifestyle or unmeasured factors cannot be fully ruled out without an RCT.
What This Means for You
The framework is operationalized by a declaration checklist that authors complete at submission. Within this tiered system, we propose minimum reporting standards by function, cross-cutting requirements, and obligations for reviewers and editors. The goal is to make AI's role in evidence generation transparent to readers, reviewers, and editors so they can evaluate the integrity of scientific findings.
The Discovery
Artificial intelligence (AI) is now being used across scientific research workflows, from data extraction and code generation to literature synthesis and manuscript drafting. Scientific journals and professional organizations agree that authors should disclose AI assistance. But disclosure that treats all AI use equally with a single generic declaration reveals little about the nature of AI's influence on evidence generation. Polishing grammar carries a different risk than extracting outcomes from clinical notes, classifying pathology, or making causal inference. We propose ATTEST (AI Transparency Tiered by Evidentiary Stakes), a framework that evaluates AI assistance by its consequences. ATTEST classifies AI use into eight broad categories of functions, each scored at one of four risk tiers (Low, Medium, High, Very High).
How It Works, Simply
This gastroenterology research focuses on AI model. Researchers evaluated it using an observational cohort study at the real-world cohort stage of medical research.
3 Key Takeaways
Clinical Takeaways- 01Core Finding
Scientific journals and professional organizations agree that authors should disclose AI assistance.
- 02Study Evidence
Tested in a observational cohort study at the real-world cohort stage.
- 03Practical Meaning
Observational cohort findings reveal real-world links, though lifestyle or unmeasured factors cannot be fully ruled out without an RCT.
Biological Mechanism & Study Design
Study Design & Cohort
Observational cohort study
Observational cohort study · Real-World Cohort
Biological Pathway & Modality
AI model
Intervention: ATTEST 2027: A Framework for AI Transparency Tiered by Evidentiary Stakes
Experimental Findings & Endpoints
Artificial intelligence (AI) is now being used across scientific research workflows, from data extraction and code generation to literature synthesis and manuscript drafting.
Scientific journals and professional organizations agree that authors should disclose AI assistance. But disclosure that treats all AI use equally with a single generic declaration reveals little about the nature of AI's influence on evidence generation.
Polishing grammar carries a different risk than extracting outcomes from clinical notes, classifying pathology, or making causal inference. We propose ATTEST (AI Transparency Tiered by Evidentiary Stakes), a framework that evaluates AI assistance by its consequences.
ATTEST classifies AI use into eight broad categories of functions, each scored at one of four risk tiers (Low, Medium, High, Very High).
Clinical Translation & Practice Context
The framework is operationalized by a declaration checklist that authors complete at submission.
Within this tiered system, we propose minimum reporting standards by function, cross-cutting requirements, and obligations for reviewers and editors. The goal is to make AI's role in evidence generation transparent to readers, reviewers, and editors so they can evaluate the integrity of scientific findings.
The Bottom Line · Clinical Pearl
“The goal is to make AI's role in evidence generation transparent to readers, reviewers, and editors so they can evaluate the integrity of scientific findings.”
Published in Hepatology (baltimore, Md.) · Current Stage: Real-World Cohort
