Yes. An AI-assisted discovery can undergo peer review, but reviewers evaluate the scientific claim and its evidence—not the fact that a model helped produce it. Human authors must disclose how AI was used, verify the work, and accept responsibility for every result. A credible assessment separates four questions: does the supplied pipeline rerun, is the result robust to reasonable choices, does it hold on new data, and does the interpretation stay within the evidence?
What it means to peer review an AI-assisted discovery
Peer review does not certify that an artificial-intelligence system is intelligent, original or trustworthy. It asks whether a clearly stated scientific claim follows from transparent methods and adequate evidence. If a model proposed a hypothesis, generated code, selected variables, analyzed data or helped write a manuscript, those contributions become part of the method that reviewers must examine.
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The accountable parties remain the human authors. The Committee on Publication Ethics (COPE) says AI tools cannot be authors because they cannot take responsibility for the work. Authors should disclose material AI use and own the accuracy, integrity and originality of the submission. The Association for Computing Machinery (ACM) similarly calls for detailed disclosure when AI contributes to research design, coding, analysis, testing, validation or archiving.
That means an AI-generated result is neither automatically invalid nor automatically reliable. It is reviewable when the claim, data, computational procedure and human oversight are documented well enough for others to assess them.
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The four tests reviewers should keep separate
| Question | What is tested | What a positive result supports | What it does not prove |
|---|---|---|---|
| Can the published computation be rerun? | Reproducibility with the original data and code | The reported pipeline can generate the reported output under the stated conditions | That the data were unbiased, the method was chosen fairly, or the finding generalizes |
| Does the finding survive a new study? | Replicability with new data, sites, times or analysts | The result is consistent beyond the original dataset and workflow | That every population or setting will show the same effect |
| Is the result stable under defensible alternatives? | Robustness to seeds, splits, preprocessing, models and analysis choices | The conclusion is not dependent on one fragile configuration | That no undisclosed choice or bias remains |
| Does the conclusion match the evidence? | Statistical and substantive validity | The wording, uncertainty and scope are justified | Causal truth, absence of hidden bias or universal generalization |
The National Academies draws the key distinction this way: “reproducibility involves the original data and code; replicability involves new data collection to test for consistency with previous results of a similar study.” A paper may pass one test and fail another, so a reviewer should report each status separately.
Step 1: Rewrite the headline as a falsifiable claim
AI systems often produce broad, persuasive language. Replace it with a statement that can be checked. Specify:
- Population or data domain: Who or what was studied, and during which period or geography?
- Task: What prediction, classification, estimation or discovery was attempted?
- Metric: Which outcome is measured, and how is it calculated?
- Comparator: What baseline, control group or existing method is the AI method being compared with?
- Uncertainty: What interval, error estimate or probability accompanies the point result?
- Scope: Is the claim descriptive, predictive, causal or mechanistic, and where is it intended to apply?
For example, “the model discovers a superior treatment rule” is too vague. A checkable version might say that a named model, trained on a defined patient cohort before a specified date, improves a prespecified metric over a clinical baseline on a later, held-out cohort, with an uncertainty interval and a stated deployment population.
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Step 2: Check data provenance before looking at performance
A high score cannot rescue an untraceable or selectively assembled dataset. Establish where every important input came from and how it entered the analysis.
Questions about the source data
- Who collected the data, under what protocol and for what original purpose?
- What inclusion and exclusion rules were applied, and were they fixed before the results were inspected?
- How were labels created, adjudicated and checked for disagreement?
- Which records, sites, languages, time periods or demographic groups are missing?
- Are the data public, licensed, confidential or generated by the authors? If access is restricted, what lawful verification route is available?
Tests for leakage and selection effects
Map the boundary between training, validation and test data at the level that matters. Near-duplicate documents, repeated patients, future information, post-outcome measurements and preprocessing fitted on the full dataset can all leak information into evaluation. For time-dependent claims, a random split may allow later information to influence an earlier prediction task.
Also ask when the benchmark was chosen. The OECD notes that releasing code and data makes checking easier, but it does not by itself rule out favorable dataset selection. A team can try many datasets and publish only the one on which its method performs best. Reviewers should request the rationale, search history or preregistered plan when the choice could affect the conclusion.
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Step 3: Demand a complete computational artifact
For complex computational work, a paper’s methods section is not enough. The National Academies states that large-data, multi-stage computations require additional information about data, code, models and the computational environment.
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- Input data, a legally permitted sample, or a script that generates an equivalent test set.
- Data dictionaries, labeling guidance, inclusion and exclusion code, and preprocessing outputs.
- Source code for training, inference, evaluation, statistical analysis and figure generation.
- Model name, checkpoint or commit identifier, fine-tuning corpus, tokenizer and relevant configuration.
- Dependency versions, operating-system details, hardware, accelerator settings and runtime assumptions.
- Configuration files, command-line arguments, random seeds and the number of runs.
- Intermediate files when nondeterministic or restricted systems prevent a clean end-to-end rerun.
- Instructions that start from a clean environment and identify expected outputs, tolerances and known failure points.
A repository link alone is not a reproducibility guarantee. The reviewer should verify that the released version is the one used for the reported tables and figures, that required checkpoints are obtainable, and that the instructions do not rely on undocumented manual edits or unavailable services.
Step 4: Reproduce the central result
Begin with the result that carries the paper’s main conclusion: the primary estimate, classification metric, table or figure. Use a clean environment rather than the authors’ configured machine when possible.
- Freeze the supplied versions. Record the code commit, model or checkpoint, data revision, dependencies, hardware and operating-system information.
- Follow the documented commands exactly. Do not silently repair missing steps; record every deviation.
- Compare the same quantity. Use the authors’ definition of the metric, aggregation rule, evaluation split and rounding convention.
- Set a tolerance before judging. Floating-point differences may be harmless, while a materially different estimate or ranking is not. The tolerance should be justified by the metric and the claimed precision.
- Repeat where randomness matters. Run the stated seeds and, if feasible, additional seeds to distinguish a deterministic mismatch from ordinary variance.
- Log failures and resource limits. A timeout, missing private endpoint, unavailable GPU or undocumented preprocessing step is evidence about reproducibility, not merely an inconvenience.
Classify the outcome precisely: exact or tolerance-level match, close but unexplained difference, partial reproduction of secondary results, or failure to run. If data cannot be shared, a secure review environment or independently generated equivalent can provide evidence, but it should be labeled a limitation rather than called an exact reproduction.
Step 5: Stress reasonable analytical choices
A single successful rerun may show only that one path through the analysis works. Robustness testing asks whether defensible alternatives lead to the same substantive conclusion.
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Vary the data and preprocessing
- Use alternative train, validation and test splits, including time-ordered or group-based splits where appropriate.
- Repeat with plausible missing-data treatments, normalization rules, deduplication thresholds and label definitions.
- Check whether removing one site, class, language, institution or influential observation changes the finding.
Vary the model and optimization
- Run multiple random seeds and report the distribution, not only the best run.
- Test reasonable hyperparameter ranges, stopping rules, prompt templates and decoding settings.
- Compare with strong, simple baselines and with ablations that remove the claimed innovation.
Use structured designs when the choice space is large
The Transparency and Openness Promotion (TOP) framework identifies multiverse and many-analyst designs as ways to expose analytical flexibility. A multiverse runs a prespecified set of defensible choices; a many-analyst design asks independent analysts to address the same question. Agreement across those paths is more informative than a single researcher-selected specification.
Step 6: Test replicability and external validity
Replication uses new data or an independent study. It is not a replacement for reproducing the original computation: if the published pipeline cannot be rerun, a later success may reflect a different implementation.
Choose a meaningful new setting
- Held-out data: A genuinely untouched portion from the same source tests leakage and overfitting.
- Later data: A future time period tests drift and prospective performance.
- Different geography or institution: This tests transportability across collection practices and populations.
- Independent collection: A new team and protocol provide the strongest check against shared implementation errors.
Define the target population before inspecting results. A model can replicate its ranking in a new hospital while being miscalibrated for that hospital’s prevalence. Report both discrimination and calibration where prediction is the claim, and explain which differences are expected from sampling variation or changed conditions.
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Step 7: Audit uncertainty, multiplicity and subgroup behavior
AI workflows can generate many candidate hypotheses, prompts, features and model variants. The more opportunities to find an attractive result, the greater the need to account for multiplicity and selection.
- Check confidence or credible intervals, standard errors and the assumptions behind them.
- Ask whether the reported interval accounts for model selection, repeated tuning or clustered observations.
- Inspect missing-data handling and whether missingness is related to the outcome or subgroup.
- Review calibration, error bars, threshold choice and clinically or scientifically meaningful effect sizes.
- Require correction or appropriate interpretation for multiple comparisons when many outcomes or subgroups were tested.
- Compare performance and error types across relevant demographic, geographic, device, language or disease subgroups.
Do not treat a non-significant subgroup difference as proof of equal performance; examine the interval and the power to detect a meaningful disparity. Conversely, an apparent subgroup effect found after extensive searching needs confirmation in new data.
Step 8: Verify disclosure and human accountability
AI disclosure should be specific enough for another researcher to understand what the tool did and to repeat analytically relevant steps. The Centers for Disease Control and Prevention (CDC) recommends naming the tool and version, describing the affected work, recording prompts or settings when relevant, and documenting human oversight. For analytic or methodological uses, it advises enough detail on prompts, settings, inputs and validation steps to support reproducibility, subject to security requirements.
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A practical disclosure can follow the CDC’s example: “[Name of AI tool] [model/version, if available] [(manufacturer, location)] was used to generate analytic code used in data processing; authors reviewed, tested, and validated all code.” Adapt that wording to state whether AI also proposed the study design, transformed data, generated labels, selected models, interpreted outputs or drafted text.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteReviewers should check citations and factual statements produced with AI. The CDC warns that citation inaccuracies can constitute research misconduct. Human review must include source verification, not just proofreading.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Reporting frameworks that make checking easier
TOP transparency and verification practices
TOP 2025 organizes seven practices: study registration, study protocol, analysis plan, materials transparency, data transparency, analytic-code transparency and reporting transparency. Each practice has levels covering disclosure, sharing and certification. TOP also defines independent results-transparency and computational-reproducibility verification, along with replication, registered reports, multiverse and many-analyst studies.
These labels are useful only when the paper states exactly what was shared and what an independent party verified. “Open science” should not substitute for a list of accessible files, versions, restrictions and checks.
TRIPOD-LLM for biomedical and healthcare language-model studies
For biomedical and healthcare LLM studies, TRIPOD-LLM, published in Nature Medicine on 8 January 2025, contains 19 main items and 50 subitems. It covers objectives; development, fine-tuning and validation stages; data preprocessing and quality checks; model version and fine-tuning details; open-science practices; results; and discussion. Its purpose is to improve appraisal, transparency and replication.
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Common failure modes and how to handle them
| Failure mode | Why it matters | Appropriate reviewer response |
|---|---|---|
| “The code is available,” but required data or checkpoints are missing | The central result cannot be reconstructed | Request a lawful access route, synthetic or equivalent test data, and an explicit limitation if exact rerun is impossible |
| Only the best random seed is reported | Performance may reflect chance | Request all stated runs and a prespecified seed-sensitivity analysis |
| Test data influenced preprocessing or prompt selection | Evaluation is contaminated | Rebuild the split and preprocessing from training data only |
| Benchmark chosen after trying many datasets | Favorable selection can mimic discovery | Ask for selection history, preregistration or replication on an independently chosen benchmark |
| One institution or time period is treated as universal | Distribution shift can reverse performance | Require external validation and narrow the stated scope |
| AI-generated citations or explanations are accepted without checking | Fabricated or inaccurate support undermines the paper | Verify every material citation and separate generated interpretation from evidence |
| A successful rerun is presented as proof of truth | Reproducibility does not establish lack of bias or causality | Report rerun, robustness, replication and interpretive validity as separate judgments |
A reviewer’s evidence record
Keep an audit table while checking the paper. It prevents a persuasive narrative from collapsing distinct kinds of evidence into one verdict.
| Item to record | Example entry |
|---|---|
| Claim under review | Exact population, task, metric, comparator, uncertainty and scope |
| Artifact version | Code commit, data revision, model/checkpoint, dependency lockfile |
| Original rerun | Main metric matched within the prespecified tolerance; figure 2 differed |
| Robustness | Result held across seeds and two defensible preprocessing choices |
| Replication | Independent site reproduced direction but not calibration |
| Unresolved limits | Restricted labels, unavailable training checkpoint, untested subgroup |
| Interpretation | Predictive association supported; causal mechanism not established |
When a check cannot be performed, name the missing evidence and its likely effect on confidence. “Not assessed because the training data are proprietary” is more informative than “validated.”
How to write the final peer-review judgment
Use calibrated language tied to the tests you actually completed:
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- Reproducible: The independent rerun produced the reported central outputs within the stated tolerance using the same data and computational procedures.
- Partially reproducible: Some outputs matched, but missing artifacts, nondeterminism or unexplained differences prevented a complete rerun.
- Robustness supported: The substantive conclusion persisted across the prespecified reasonable alternatives that were tested.
- Replicability supported: An independent dataset or study produced a consistent result for the defined population and outcome.
- Interpretation constrained: The evidence supports an association or prediction, but not a causal, mechanistic or universal claim.
A paper can be publishable with limitations if those limitations are visible and the conclusion is narrowed accordingly. Conversely, a polished paper with a high score should not receive a strong endorsement when its data provenance, leakage controls or computational artifact are opaque.
What peer review can—and cannot—establish
Peer review can test logical coherence, methods, statistical analysis, provenance and whether the evidence supports the stated conclusion. Reproduction can show that the published pipeline yields the published output. Replication and external validation can test whether the finding survives new data or settings.
None of these alone proves causal truth, absence of hidden bias or universal generalization. The most defensible verdict says exactly which layer has been checked: “the code reruns,” “the result is stable,” “the finding replicated in a defined new setting,” or “the interpretation is justified within a limited scope.”
Quick Recap
Printable checklist
- Is the claim falsifiable and bounded by population, task, metric, comparator, uncertainty and scope?
- Are data origin, inclusion rules, labels, preprocessing and missingness documented?
- Are train, validation and test boundaries protected from leakage?
- Can an independent party obtain the data or a lawful equivalent?
- Are code, checkpoints, configurations, dependencies, hardware assumptions and seeds identified?
- Can the central table, figure or estimate be reproduced within a stated tolerance?
- Were reasonable seeds, splits, preprocessing, stopping rules and model choices tested?
- Was the result evaluated on held-out, later, geographically different or independently collected data when generalization is claimed?
- Are uncertainty, multiplicity, calibration, missing data and subgroup behavior addressed?
- Does the AI disclosure name the tool, version, purpose, relevant prompts or settings and human validation?
- Are citations and generated factual statements verified by people?
- Does the conclusion distinguish rerunning, robustness, replication and interpretation?
- Are every failed check, restricted artifact and remaining uncertainty reported plainly?
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