The Tool Desk
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What AI can and cannot do in research
AI assistance spans different tasks: a language model may help edit prose or sift literature, while a predictive model may analyze data for a defined purpose. The limits depend on the task, the data, and how the system is evaluated. A polished answer is not proof that its facts, reasoning, or citations are sound. Treat AI output as a lead for investigation or a tool-assisted result to validate—not as a substitute for primary evidence or scientific judgment.
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There is no universal error rate or single tool that can be declared reliable for every research task. A workflow appropriate for editing a human-authored paragraph is not automatically appropriate for interpreting participant data, screening a systematic review, or generating results. Evaluate the specific use, and apply the relevant institutional, funder, publisher, and legal requirements.
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The practical question is not simply whether a tool is “good” or “bad.” Ask what decision it is being used to support, what evidence the output depends on, and whether another researcher can inspect how the result was reached. A tool can save time on a bounded task while still requiring human verification at every point where its output affects study selection, analysis, or a conclusion.
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Why fluent AI output can still be wrong
Language models can produce explanations that sound authoritative while containing factual errors, outdated information, or references that do not exist. NIH and HHS Office of Research Integrity guidance advises researchers to confirm accuracy and appropriately cite references. A generated citation is a claim to verify, not evidence that a paper exists or supports the point.
Check claims against the underlying source
- Locate the cited paper or official record independently; do not rely on the model’s citation alone.
- Confirm that the source exists and that its authors, title, publication details, and date are correct.
- Read the relevant passage, result, or methods and check that it supports the specific claim being made.
- Check whether the source is current enough for the question, and distinguish a source’s findings from the model’s interpretation.
NIH and HHS ORI identify hallucinated references as a research-integrity concern. If a source cannot be confirmed, do not cite it as real. If the AI has summarized a real paper, verify the summary against the paper itself.
This checking matters even when a reference is real. A model may attach a genuine paper to a claim the paper does not make, omit a qualification, or conflate a study’s result with a broader conclusion. For consequential claims, inspect the original article or official record and cite that source rather than citing an AI-generated summary. NIH’s guidance is direct: researchers should appropriately cite references and carefully review and confirm that information is accurate.
Bias, performance, and generalizability
Model performance can vary across populations, settings, and subgroups. NIH cautions that predictive performance may differ with population, environment, sex, age, and comorbidities; a result averaged across one dataset does not establish validity for another population or use. Training and evaluation data can also leave important groups or conditions underrepresented.
Before applying a model beyond the data or setting in which it was evaluated, ask whether the evaluation population represents the intended use. Test relevant groups and settings independently where possible, report the populations and conditions covered, and state limitations clearly. Do not present a headline accuracy figure as proof of performance for a different group or environment.
For example, evidence that a model performs adequately in one environment does not establish that it will perform similarly in a different clinic, community, or research setting. The same caution applies when the intended population differs by age, sex, comorbidities, or other relevant characteristics. Researchers should describe the population and conditions actually assessed, and avoid implying that performance generalizes more broadly than the evaluation supports.
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Opaque steps can weaken evidence synthesis
Some AI workflows make intermediate decisions difficult for colleagues to inspect. That opacity matters when a system helps screen studies, extract evidence, or synthesize findings: users need to understand how records were handled and whether outputs introduced omissions, bias, overfitting, or fabricated material.
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In practice, make clear whether automation was used during screening, extraction, or synthesis, rather than describing the review as wholly manual. Preserve enough information for a colleague to follow inclusion decisions back to the evidence. Efficiency is useful, but it does not resolve whether a record was wrongly excluded, an important finding was missed, or a generated summary is supported by the underlying studies.
Reproducibility requires more than naming the tool
An AI-assisted result may be hard to reproduce if the model or version changes, or if prompts, settings, source data, code, preprocessing, and validation steps are not recorded. A paper’s methods section may not by itself capture everything needed to reconstruct a complex computational workflow.
NIH’s 2026 intramural guidance says integral AI use should be disclosed to the team, documented in the laboratory record, and reported in publications or presentations. The National Academies’ report on computational reproducibility explains that replication can require data, code, methods, and details of the computational environment.
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Record the details needed to inspect the work
- The tool and model used, including its version when available.
- The task for which AI was used and where it entered the research workflow.
- Relevant prompts, settings, and other parameters that affect the output.
- Input data sources, preprocessing, code, and the computational environment where applicable.
- How outputs were checked, validated, or corrected, and the limitations that remain.
The amount of detail depends on whether AI use was incidental or integral to the result. If it could affect a research decision, analysis, or reported conclusion, preserve enough information for colleagues and readers to understand and assess that contribution.
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Recording only a product name may not be enough: a model or its settings can change, and a result may also depend on the exact input data and processing steps. A reproducibility record should therefore explain how AI fit into the workflow and what humans did to check its output. Where the workflow includes code or substantial computation, record the relevant computational environment as well as the methods. These details help distinguish a result that can be independently assessed from one that depends on undocumented steps.
Protect confidential, personal, and unpublished information
Submitting research material to an external AI service can create privacy, confidentiality, ownership, or reuse risks. Do not enter identifiable participant data, confidential manuscripts, unpublished concepts, or proprietary material unless the applicable policy, authorization, and tool protections permit it.
NIH trainee guidance limits public generative AI tools to public-domain information and excludes personally identifiable information and consented clinical research data. NIH intramural guidance says to review terms related to ownership, reuse, and opt-out; clinical personally identifiable information analyses must remain in protected systems. These examples are specific to NIH guidance, not a substitute for your institution’s rules or the terms governing a particular dataset.
Before entering research material
- Identify whether the material includes personal, clinical, confidential, unpublished, or proprietary information.
- Check institutional and project rules, consent conditions, data-use agreements, and any funder requirements that apply.
- Review the tool’s terms for data retention, reuse, ownership, and opt-out options.
- Use an approved protected environment where required; if authorization or safeguards are unclear, do not submit the material.
The fact that a tool is publicly accessible does not establish that it is appropriate for research data. A participant’s consent, an agreement governing a dataset, or a local policy may impose conditions beyond the tool’s own terms. In particular, NIH trainee guidance treats public generative AI tools as suitable for public information, not personally identifiable information or consented clinical research data. When clinical PII analysis is involved, NIH intramural guidance requires protected systems.
Human accountability, authorship, and integrity
AI cannot take responsibility for a paper’s claims or meet NIH authorship criteria for AI language tools. Springer Nature likewise states that scholarly accountability remains with human authors. Researchers must verify the substance of work produced with AI, disclose material use as required, and ensure that authors can defend the paper’s methods and conclusions.
NIH and HHS ORI identify potential misconduct concerns including fabricated data presented as empirical, undisclosed AI image alteration, nonexistent references represented as real, and undisclosed copying. The issue is not simply whether a tool was used: researchers must not misrepresent generated content or evidence as something it is not. Consult the rules of the relevant institution, funder, and journal, since disclosure and permitted-use requirements can differ.
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Human responsibility includes more than proofreading. A research team must be able to explain why a claim follows from the evidence, whether a generated image or result was altered, and what role the tool played. Presenting invented data as observed results, treating a nonexistent citation as a real source, or concealing copying can create serious integrity concerns. Disclosure alone does not make inaccurate or unsupported content acceptable; the underlying work still needs to be checked and represented honestly.
Where limited assistance may fit
NIH guidance describes context-specific assistance such as editing human-authored text, brainstorming before a human draft, meeting notes, and literature sifting under its intramural policy. Such permission does not make AI-generated ideas or conclusions automatically reliable. Grant applications have additional restrictions: NIH’s broader reminder allows limited application-preparation assistance but says substantially AI-developed applications or sections are not considered the applicant’s original ideas.
Check the policy that governs your institution and submission before using AI in a grant, manuscript, review, or analysis. In any allowed use, separate assistance with a task from responsibility for the resulting scientific content.
These examples are not a blanket endorsement of every tool or every use. Rules can depend on the specific submission and institution, and grant preparation is not interchangeable with editing a human-written paragraph. If an intended use is not clearly covered, check the applicable policy before proceeding. Even where a use is permitted, the researcher remains responsible for verifying the content and ensuring that the work reflects the applicant’s or authors’ own ideas as required.
How to evaluate an AI-assisted research workflow
Compare tools and workflows against the same research task rather than choosing on fluency or convenience alone. These criteria reflect risks and controls identified in NIH, publisher, and National Academies guidance:
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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 minute- Task-specific accuracy: Does independent checking show that outputs are correct for the intended task?
- Currency and traceability: Can claims be traced to current, inspectable sources?
- Population fit: Has performance been evaluated on the intended population and relevant subgroups?
- Reproducibility: Can the team record the model, version, settings, inputs, and validation steps needed to inspect the result?
- Data protection: Do privacy, confidentiality, retention, and reuse terms fit the material being handled?
- Policy fit: Does the workflow comply with institutional, funder, publisher, consent, and data-use requirements?
A system that performs well on one task or dataset is not thereby validated for another. If a workflow cannot be checked, documented, or used with appropriate safeguards, its convenience does not make it suitable for scientific evidence.
These are comparison dimensions, not a universal ranking or product recommendation. A tool may be suitable for public literature sifting but unsuitable for confidential data; another may offer useful analytical support but lack enough traceability for a particular claim. Compare the whole workflow—including the data and human review—not just the model’s fluent responses or a single performance figure. No universal AI error rate is established by the guidance discussed here, so a number from one task should not be treated as a general measure of scientific reliability.
Frequently Asked Questions
Can AI be trusted in scientific research?
Not without task-specific checks. AI can assist with bounded tasks, but fluent output is not proof that a claim, interpretation, or citation is correct. Verify substantive claims against primary or official sources, and validate analytical outputs on data representative of the intended use.
Can AI make up research citations?
Yes. NIH and HHS Office of Research Integrity identify hallucinated references as a concern. Check independently that each cited source exists and that its details are correct, then read the relevant passage to confirm it supports the claim. Do not cite an unconfirmed reference as real.
Does AI make research less reproducible?
It can, if researchers omit details needed to inspect or repeat the workflow. Record integral AI use, the tool and model version when available, relevant prompts and settings, input data, code and preprocessing, validation steps, and computational environment details where applicable. NIH guidance calls for integral use to be documented and reported.
Can I put unpublished or patient data into an AI tool?
Only if the applicable policy, authorization, consent conditions, and tool protections allow it. NIH trainee guidance excludes PII and consented clinical research data from public generative AI tools, and NIH intramural guidance says clinical PII analyses must remain in protected systems. Check local rules and terms on retention, reuse, ownership, and opt-out; if unclear, do not submit the material.
Can researchers use AI to write a grant application?
Rules depend on the governing institution and submission. NIH guidance allows limited application-preparation assistance, but says substantially AI-developed applications or sections are not considered the applicant’s original ideas. Check the applicable policy and remain responsible for the content.
Does AI count as an author on a scientific paper?
NIH says AI language tools do not meet its authorship criteria, and Springer Nature states that scholarly accountability remains human. Researchers must verify claims, follow relevant disclosure rules, and ensure human authors can defend the methods and conclusions.
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AI can support scientific work, but it cannot certify its own accuracy, generalizability, or integrity. Use it only where the task and data permit; independently verify claims and outputs, protect confidential information, document integral use, and follow the policies that apply. The research team remains accountable for the evidence and the conclusions.
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