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Evaluate an AI tool against a defined scientific task—not a broad claim that it is “good at research.” Test it on representative examples, compare candidates under the same conditions, verify important outputs independently, and check whether its data handling, reproducibility, and oversight requirements fit your project. The right choice depends on your discipline, workflow, and the consequences of an error; there is no universal product ranking or score that settles the question. Researchers remain accountable for scientific judgment and the integrity of the work.
What does a reliable AI tool for research need to do?
“Reliable” is task-specific. A system that helps organize public papers may be useful for that purpose but unsuitable for interpreting clinical records, analyzing a sensitive dataset, or supporting a consequential scientific conclusion. Judge the tool by evidence from the work you intend to give it, not by its marketing, a polished demonstration, or its performance on an unrelated task.
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Start by describing the intended use in a sentence: what the tool will receive, what it is expected to produce, who will review the result, and how that result may affect the work. For example, “summarize the methods sections of a defined set of public papers so a researcher can compare their reported inclusion criteria” is testable. “Help with a literature review” is too broad to reveal what success or failure means.
The OECD recommends clearly reasoned evaluation and explicit descriptions of limitations when assessing AI in science. Its guidance also emphasizes treating algorithms equally in a study and documenting algorithmic, implementation, and observation limitations. OECD guidance on reproducibility in AI research is a useful foundation for designing a fair comparison.
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Which criteria should I use to compare tools?
Use criteria tied to the actual task and its risks. The questions below are a practical checklist, not a validated scoring instrument or universal ranking. Weight the criteria according to what could go wrong in your discipline; for some work, an unacceptable data-governance issue should rule out a tool regardless of its convenience.
| Criterion | What to test | What a concerning result looks like |
|---|---|---|
| Task performance | Does the tool handle representative inputs and produce results suitable for the intended use? What errors matter to the planned decision? | It fails on common cases, or its mistakes could change an interpretation or conclusion without being readily noticed. |
| Evidence and traceability | Can you trace claims, references, summaries, and calculations to primary sources, underlying data, or code? | It presents unsupported claims confidently, supplies references that cannot be found, or gives no workable way to check its output. |
| Reproducibility | Can the team record the tool or model version, inputs, settings, and validation steps well enough to understand the workflow later? | Important settings are hidden or change without a record, making it difficult to explain how a result was produced. |
| Data governance | Where are inputs processed and retained? Who can access them? Are the inputs permitted under consent, IRB, institutional rules, contracts, and privacy obligations? | The data handling is unclear, or the proposed use conflicts with a requirement governing the data. |
| Bias and limitations | How does the tool respond to uncertainty, missing information, or out-of-scope requests? Could data or prompt bias distort the answer? | It conceals uncertainty, fills gaps with unsupported material, or performs unevenly across relevant cases. |
| Human oversight and workflow fit | Who reviews outputs, what expertise is needed, and does the time saved justify verification and integration work? | Review demands erase the practical benefit, or no qualified person can take responsibility for checking consequential outputs. |
Do not collapse these dimensions into one impression. A tool can be useful for a low-risk, easily checked task and inappropriate for a task involving confidential inputs or hard-to-detect errors. Treat requirements such as permission to process data and an ability to verify important claims as gates: a strong result elsewhere does not compensate for a failed gate.
How do I run a fair evaluation?
Use the same representative tasks and criteria for each candidate, include a baseline where practical, and record enough detail to interpret the results. This sequence translates the guidance into a working evaluation; it is not a formally validated benchmark protocol.
- Define the task and acceptable failure modes. State what the AI may do, what it must not do, what counts as a useful output, and which mistakes are unacceptable. Decide in advance whether a result will inform a draft, a data-analysis step, a decision, or only an exploratory activity.
- Choose representative examples. Select inputs that reflect the material, range, and difficulty the team expects in real use. Include cases with missing or ambiguous information when those occur in the workflow. Where possible, include examples with known answers or a conventional workflow that can serve as a baseline.
- Apply the same tasks and criteria across candidates. Keep prompts, input materials, and evaluation conditions consistent where the tools allow it. If a product requires different settings or input formats, record the difference rather than quietly treating outputs as directly equivalent.
- Record versions and settings. Note the tool and model version when available, date of use, relevant settings, prompt or instructions, and input type. Products can change, so a result from one version should not automatically be treated as evidence about a later one.
- Check outputs against independent evidence. Verify references against original papers; check calculations, methods, and results against the underlying data or code; and use qualified expert review when the task requires subject-matter judgment. A fluent answer is not verification.
- Assess data handling before using nonpublic material. Establish where inputs are processed, whether they are retained, and who can access them. Resolve whether the proposed use is allowed by consent, IRB requirements, institutional policies, contracts, privacy obligations, and intellectual-property rules before uploading data.
- Assess the whole workflow, not just generation speed. Include time and effort for checking, correction, documentation, and integration. Determine whether the demonstrated benefit remains worthwhile once those responsibilities are included.
- Document material use and follow applicable disclosure rules. Keep a record sufficient to explain what the tool did and how its outputs were checked, subject to security and data-sharing constraints. Check current requirements from your institution, funder, and journal.
A small, well-chosen evaluation is more informative than trying many unrelated prompts without a plan. Preserve failed examples as well as successful ones: they can show where a tool is unreliable, whether errors are detectable, and whether the proposed human review is sufficient.
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How can I tell whether an AI-generated answer is accurate?
Verify claims at their source and match the verification method to the output. AI-generated citations, summaries, analysis, and text can be wrong or misleading; plausibility, confidence, and citation formatting do not establish accuracy.
For literature searches, citations, and summaries
Open each cited paper and confirm that it exists, that the bibliographic details are correct, and that it supports the specific claim attributed to it. Read the relevant passage rather than relying on a title or abstract alone. Check whether a summary preserves the original authors’ qualifications, methods, and limits instead of turning a narrow result into a broader conclusion.
NIH and HHS Office of Research Integrity staff advise researchers to cite references appropriately and carefully review and confirm accuracy. NIH’s May 14, 2026 reminder also flags nonexistent references, inaccuracies, plagiarism, and undisclosed image alteration as integrity concerns. Read the NIH reminder for NIH-supported research.
For analysis, calculations, and interpretations
Re-run calculations where possible and compare them with the underlying data, code, or an independently established answer. Check that the tool applied the intended method, used the right variables and units, and did not omit a relevant condition. For an interpretation, ask a qualified researcher to assess whether the conclusion follows from the evidence and whether alternative explanations or limitations have been overlooked.
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For images and other research materials
Keep track of whether an AI system generated or altered an image and what, if anything, changed. Do not treat image outputs as transparent edits merely because they look plausible. Confirm applicable institutional, funder, and journal requirements for image processing and disclosure before using an altered image in research or publication; the NIH reminder specifically identifies undisclosed image alteration as an integrity concern.
Document what was checked and by whom at a level appropriate to the task. If a claim cannot be traced to a source or validated against data, code, or expert judgment, do not present it as established merely because an AI tool produced it.
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Establish permission and data handling before testing a tool with nonpublic material. A useful output does not make an otherwise unauthorized disclosure acceptable.
- Identify the data category: public, confidential, personal, clinical, consented, licensed, or otherwise restricted.
- Find out where the service processes inputs, how long it retains them, and who may access them; read the applicable service and institutional terms rather than assuming that a “private” label settles the question.
- Check consent language, IRB requirements, institutional policies, contractual restrictions, privacy obligations, and intellectual-property terms that apply to the specific material.
- Ask the responsible institutional office or data steward when the permitted use is unclear; do not test a disputed use by uploading a real record.
- Use public or appropriately de-identified material for an evaluation only when the governing rules permit it. De-identification alone does not establish that every proposed use is allowed.
NIH trainee guidance says external or public generative AI tools may be used only with public data and excludes personally identifiable information and consented clinical research data. That is scoped NIH guidance, not a universal rule for every institution or researcher. Check the applicable rules for your own role, funder, organization, and data. See NIH trainee guidance on AI use. The National Academies’ responsible-conduct materials also address responsible AI use in research. Read the National Academies’ responsible-conduct materials.
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How do reproducibility and documentation affect the decision?
Record enough about a material AI-assisted step that another researcher can understand what happened, how the result was checked, and what limitations apply. The record should be useful for interpreting or reconstructing the workflow without violating security or data-sharing restrictions.
Depending on the task, document the tool and model version if available, date, input type, relevant settings, prompts or instructions, output used, human review, and validation steps. Preserve the evidence needed to support consequential claims, such as the original paper, data, code, or the reviewer’s correction. If a product does not expose a detail that matters to understanding the result, note that limitation instead of implying the process is fully reproducible.
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The National Academies’ reproducibility guidance calls for clear, specific methods and computational details. See the National Academies’ discussion of reproducibility. Documentation does not guarantee that a result is correct; it makes the process more inspectable and its limitations easier to evaluate.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Who is responsible for AI-assisted scientific work?
The researchers conducting and reporting the work remain responsible for its scientific judgment and integrity. An AI system can assist with a task, but it cannot take responsibility for validating a claim, deciding whether a method is appropriate, or ensuring that a report meets relevant rules.
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The National Academies states that “AI tools are used to assist, not replace, scholarly review and authorship.” NIH and European Commission guidance likewise say AI systems should not be authors. Read the National Academies AI statement and the European Commission’s guidelines on responsible use of generative AI in research.
Before using a tool in work that may be submitted, funded, or published, check current disclosure and authorship requirements from the relevant journal, funder, and institution. Policies can differ by organization, field, and data regime. NIH’s Ninth Edition intramural research-conduct guide was published in 2026, while its May 2026 reminder concerns NIH-supported research; neither should be treated as a universal policy for all research settings. See the NIH Ninth Edition guide. UNESCO’s guidance, originally published September 7, 2023 and updated on its page January 16, 2026, frames generative AI use through human-centered practice, privacy, and validation as tools and policy develop. Read UNESCO’s guidance.
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Adopt it only for the specific task your evaluation supports, with safeguards matched to the consequences of failure. If it performs adequately on representative examples, its outputs can be checked, its data use is permitted, and the whole workflow has a defensible benefit, a limited and documented use may be reasonable. If a critical condition fails, narrow the use, change the workflow, or do not use the tool for that task.
- Proceed for a defined use when evidence from your examples supports the task and appropriate review is practical.
- Limit the use when performance is useful only for lower-risk or exploratory work, or when a human must substantially verify and correct its output.
- Do not use it with the proposed inputs when permission, privacy, confidentiality, or contractual requirements are unresolved or incompatible with the service.
- Re-evaluate after a material change to the tool, model, settings, data, or intended task, because the original evaluation may no longer describe the workflow.
No directly applicable statistic in the cited official and intergovernmental guidance quantifies AI’s effect on scientific quality, accuracy, or productivity. Treat any claim of a general benefit or risk as something that needs evidence for the particular tool and use case, rather than assuming a universal improvement.
Frequently Asked Questions
Should I use AI to write a scientific paper?
That depends on your institution’s, funder’s, and journal’s current policies and on what the tool does. If permitted, keep human authors responsible for the claims, verify any generated content, and follow the applicable disclosure rules.
Can I use AI to screen papers for a systematic review?
Treat screening as a defined task and assess it on a representative set of records with decisions checked against your review protocol. Whether AI-assisted screening is acceptable for a particular review depends on the field, protocol, and publication or funder requirements.
How often should I repeat an evaluation of an AI tool?
Repeat or update the evaluation when a material change could affect the result, such as a new model version, different settings, a change in data type, or a broader intended use. Keep the original record so the team can distinguish evidence about the earlier workflow from evidence about the new one.
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