The Tool Desk
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Start by deciding what AI should—and should not—do
Define the research question and identify the specific task where AI might help, such as drafting code, suggesting a visualization, or generating candidate explanations. Keep the scientific decisions with the research team: what counts as an observation, which records to exclude, which assumptions are acceptable, and what the results mean.
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Treat generated code, calculations, interpretations, and references as provisional. A fluent explanation is not evidence that the analysis is correct. NIH describes research rigor as applying to design, methods, analysis, interpretation, and reporting, and says reproducibility by multiple scientists helps validate results. See NIH’s guidance on rigor and reproducibility.
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Check data permissions before sending prompts or files
Classify the data before choosing a tool: public, sensitive, identifiable, governed by consent or a data-use agreement, or restricted to controlled access. Review the applicable consent terms, repository conditions, ethics or IRB requirements, institutional security rules, funder requirements, laws, and journal policies. Also check the AI service’s terms: where prompts and files are processed, how long they are retained, whether they may be used for training, who can access them, and whether the service is approved for that data class.
#1 Best Overall
NIH’s 2026 Guidelines for the Conduct of Research in the Intramural Research Program tell NIH intramural scientists to learn and follow relevant restrictions on internal or external AI systems. That is guidance for NIH intramural researchers, not a universal rule for every institution; other researchers should consult the policies that govern their work. Read the NIH intramural research guidelines (Ninth Edition, 2026).
Personal, clinical, and controlled-access data
Do not upload personal or controlled-access data to a public AI tool unless the governing rules specifically authorize that use. NIH says potentially person-traceable information must not be uploaded into external AI systems, and clinical personally identifiable information analyses must be performed inside protected electronic health record systems. NIH treats external access to data or text as disclosure, so a task that seems low risk does not make a public tool an appropriate destination. These are NIH-specific directions; they should not be mistaken for a complete account of the law or policy in every jurisdiction. See NIH Notice NOT-OD-25-081.
Rank #2
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For human genomic data covered by the NIH Genomic Data Sharing Policy and a Data Use Certification, NIH’s March 28, 2025 notice says public generative AI tools may not receive controlled-access data through prompts or other interfaces. Restrictions also apply to AI models and derivatives based on such data. Consult the notice and the relevant Data Use Certification before processing or sharing anything derived from the dataset.
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De-identification does not settle the question
Removing direct identifiers does not necessarily eliminate privacy risk. NIH warns that information considered de-identified under common standards may still support identity inferences when combined with other information. Consider the data type, processing level, possible linkage, and intended audience; controlled-access sharing may be more appropriate for participant data. NIH’s privacy supplement for sharing human research participant data outlines factors for these decisions.
Use AI output as a proposal, then test it
When an AI tool suggests code or an analytical choice, inspect what it does before running it in the intended environment. Compare the result with the source data and an appropriate independent check. Preserve the decisions and reasoning that shape the analysis rather than accepting a generated interpretation at face value.
- Code and transformations: inspect the operations, inputs, assumptions, missing-value handling, and units; test on known cases where possible.
- Calculations and reported numbers: recompute key values independently and check that figures and tables match the analysis output.
- Preprocessing and exclusions: document how records were filtered, transformed, or excluded, and assess whether reasonable alternatives change the result.
- Models and interpretations: examine assumptions, subgroup behavior, plausible alternative explanations, and whether the model or training population fits the research population.
- References and visuals: verify that cited sources exist and support the claim. Check that images or data visualizations have not been altered in ways that misrepresent evidence.
NIH identifies fabricated data, nonexistent references, undisclosed copied text, and undisclosed AI image alteration as research-integrity risks. Its 2026 reminder for NIH-supported research advises researchers to check facts and references and describe AI use. UNESCO’s guidance for generative AI in education and research also emphasizes human-centered use, privacy, ethical validation, safety, and equity.
Rank #4
Keep a record another researcher can review
Record enough to explain how the result was produced and allow someone else to assess or repeat the work. The record should match the significance of AI’s role; a tool that drafts code for a consequential analysis deserves more detail than an incidental wording suggestion.
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- AI tool and model version where available, relevant settings, and the purpose for which it was used.
- Material prompts or instructions, generated code, and the changes made before use.
- Data cleaning, transformations, exclusions, analytical choices, and the environment used to run the work.
- Human review, independent checks, validation results, and unresolved limitations.
Where possible, rerun the documented pipeline from preserved inputs before sharing results. NIH’s 2026 intramural guidelines emphasize transparency and reproducibility. They also specify that AI-generated synthetic data included in publications or presentations must be identified as AI-generated, justified in the methods, and documented with its processing steps.
Best Value
Disclose AI use according to the rules for the work
Check current institutional, funder, repository, and journal policies before submitting or presenting results. Disclosure expectations differ and may change. NIH’s Office of Extramural Research advises researchers to describe AI use in applications, manuscripts, and presentations, including its role in research or data analysis. Its 2026 intramural guidance treats some ordinary uses—such as routine text editing, search, and brainstorming or logistical help—as generally outside its disclosure scope, with qualifications for particular versions or parameterized applications. Those distinctions are NIH guidance, not a universal disclosure rule.
When disclosure is required, describe what the tool did: for example, whether it drafted code, assisted with analysis, generated a visualization, or contributed to interpretation. State the checks performed and identify synthetic data clearly. Do not imply that a tool performed or validated work that the research team did not verify. NIH’s living AI policy resource and the policies of the target journal and institution are useful starting points.
Choose a tool only after the data rules are clear
There is no evidence-based product ranking that determines which AI tool is suitable for every scientific dataset. Once the permitted data classes are established, compare candidate tools on the factors that matter to the task and governing rules:
- Whether the tool is approved for the data class and intended use.
- Where prompts and files are processed, retention and training terms, and access controls.
- Whether outputs, code, settings, and audit records can be exported and reviewed.
- How stable the model or version is and whether changes can be tracked.
- Whether the tool fits the analytical task without obscuring methods or limiting reproducibility.
A convenient interface or a convincing answer does not establish permission, accuracy, or scientific validity. Make those judgments from the applicable rules and independent checks.
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