Separate reporting from interpretation
Establish what the paper actually reports before evaluating whether the conclusions are supported by the design and results.
AI peer review
Critique a research paper with a structured second reader
SinaPilot Review examines a readable paper for strengths, limitations, statistical concerns, potential bias, conflicts of interest, and open questions. Use the output to focus your own close reading, not to outsource editorial judgment.
No credit card required. Verify AI-generated outputs against the source.
Readable paper
- Methods, results, tables, disclosures, and conclusions
Critical appraisal
- Map claims to reported evidence
- Check design and statistical logic
- Surface limits, bias, and conflicts
Structured review
- Strengths
- Concerns
- Questions to verify
What it changes
A clearer path from paper to evidence
Look beyond the abstract
Review methods, outcome definitions, analyses, limitations, and disclosures that are easy to miss in a fast abstract-level read.
Turn concerns into checks
Use explicit limitations, statistical issues, and open questions as a prioritized list for your own verification.
How it works
From source to a result you can check
SinaPilot keeps the workflow explicit so you can see what went in, what the system produced, and where human verification belongs.
- Step 1
Add a readable paper
Upload a usable PDF or open a paper already available in your SinaPilot workspace. Full text gives the review more evidence than metadata alone.
- Step 2
Map the study
The review identifies the question, design, population, interventions or exposures, outcomes, analyses, and principal claims.
- Step 3
Run a structured critique
SinaPilot examines strengths, limitations, potential bias, statistical reasoning, conflicts, and questions left unresolved by the paper.
- Step 4
Check every consequential finding
Return to the methods, tables, figures, supplements, protocol, or registration before using a generated concern in a review or decision.
Structured output
What you get
Review organizes critical appraisal into a consistent set of sections, so both positive evidence and genuine concerns remain visible.
- Reported strengths that deserve credit
- Design and reporting limitations
- Potential sources of bias and threats to validity
- Statistical concerns, including outcome and multiplicity issues when supported by the paper
- Conflict-of-interest and funding observations based on available disclosures
- Open questions and claims that require manual verification
Use cases
Built for real research work
Preparing a journal review
Use a structured second pass to find areas that deserve closer inspection before drafting your own confidential comments.
Evaluating evidence for practice
Check whether the population, comparator, endpoint, effect estimate, uncertainty, and follow-up support the practical claim being made.
Teaching critical appraisal
Compare the generated critique with a student or team assessment and discuss where the evidence supports or weakens each point.
Human verification
What to verify
An AI review can improve consistency and attention, but it cannot assume the responsibilities of a qualified reviewer, statistician, editor, or clinician.
- Follow the journal or institution's confidentiality and AI-use rules before uploading an unpublished or sensitive manuscript.
- A concern is a prompt to inspect the source, not proof of an error. Verify it against the paper, supplement, protocol, and analysis plan.
- Absence of a flagged issue does not establish validity, and specialist methods may require domain or statistical expertise beyond a general review.
- Do not use the output as an autonomous accept, reject, clinical, regulatory, or funding decision.
Questions
Frequently asked questions
What does SinaPilot check in a research paper?
Review is organized around strengths, limitations, potential bias, statistical concerns, conflicts of interest, and open questions. The available full text determines how much evidence it can examine.
Can SinaPilot write a journal peer review for me?
It can provide a structured critique to support your reading. You remain responsible for source verification, subject-matter judgment, the journal's policies, confidentiality, and the final review you submit.
Will the tool invent a problem if it finds none?
The workflow is designed to distinguish supported concerns from unsupported ones and can report that no issue was found. You should still verify both flagged findings and apparent absences.
Does AI peer review replace a statistician?
No. It can surface questions about analyses and reporting, but complex designs and models may require review by a statistician with access to protocols, analysis plans, code, or data.
Read the practical guides
Explore the rest of SinaPilot
Read research critically, not just quickly
Start with AI Review, keep the source close, and move from isolated papers to a research workflow you can inspect.
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