AI for Health Research: Evidence Workflows and Review

AI tools support health researchers in the time-intensive middle stages of a project: screening large sets of abstracts, summarizing research on a topic, identifying gaps in the literature, and organizing evidence across multiple studies. These are tasks where AI saves meaningful time. They are also tasks where the accuracy of the output matters enough that every step requires qualified human review.

The record that keeps a health research project reliable, which sources were included and excluded, which decisions were made at each stage, and what remains unresolved, should live outside any single AI session. Tools like fenbs keep that record available to every session that continues the work.

Literature screening and abstract review

Systematic and scoping reviews involve screening hundreds or thousands of abstracts against inclusion and exclusion criteria. AI tools can assist by applying criteria to a batch of abstracts and flagging those that likely meet them. This speeds the initial pass, but human review of the AI's decisions is essential before any exclusions are finalized.

The key inputs are well-specified criteria. A model given vague criteria produces inconsistent screening. Criteria defined precisely, by population, intervention type, outcome measures, and study design, give the model enough structure to apply them consistently. Even then, ambiguous cases require a human screener to make the call.

Document the screening criteria and the version of the criteria used. If the criteria are revised mid-project, record the revision date and the reason. A reviewer evaluating the methodology later needs to know exactly what criteria were applied at each stage.

Evidence synthesis and gap identification

AI can summarize the findings of a set of studies, identify where studies agree or disagree, and flag gaps in the evidence base for a particular question. Given a collection of abstracts or full texts, it can produce a structured overview faster than a researcher reading each one independently.

The limitations are important. AI models can mischaracterize a study's findings, confuse correlation with causation in a summary, or miss the qualifications authors put on their conclusions. Any claim about a specific study's findings that appears in a report or publication must be checked against the original text.

AI is also useful for generating a structured set of questions about a body of evidence: "What aspects of this topic have not been adequately studied?" "Which populations are underrepresented in the included studies?" Answers to those questions still require researcher judgment, but having a draft starting point accelerates the analysis.

Data extraction and organization

Extracting data from published studies, pulling outcome measures, sample sizes, follow-up periods, and effect sizes into a structured format, is repetitive work that AI handles reasonably well. Give the model a table structure and a set of studies, and it can populate initial extraction tables that a researcher then verifies against each source.

Verification is not optional. AI-extracted numbers require checking against the original study. The cost of an incorrect number in a meta-analysis or systematic review is significant. Use AI to create the extraction structure and a first-pass population; use human review to confirm every number before it is used in analysis.

Research coordination across a team

Health research projects often involve multiple researchers working across different parts of a study at the same time. One person handles screening, another handles data extraction, a third handles quality assessment. Keeping decisions and open questions visible to the whole team prevents duplication and contradictory judgments.

Research coordination decisions, the inclusion criteria for this review, the quality assessment tool chosen, how disagreements between screeners are resolved, belong in a shared record. Recording them once means every team member and every AI session works from the same rules.

Regulatory and ethical considerations

Health research involving human subjects operates under regulatory frameworks including the Common Rule in the US, institutional review board requirements, HIPAA for health data, and journal-specific data reporting standards. AI tools cannot confirm regulatory compliance, and no AI tool substitutes for the oversight of an IRB or ethics committee.

Non-sensitive research decisions, protocol structure, literature inclusion criteria, analysis approach, and publication planning, can be supported by AI and recorded in shared tools. Patient data, identifiable information, and health records must not be entered into general-purpose AI tools. Follow your institution's data governance policy before deciding what to include in any AI-assisted workflow. When in doubt, use de-identified or synthetic data for tool testing and reserve access to real records for compliant systems only.

How to do this with fenbs

1. Create a free account and add a project for the review or research program. Include the research question and the current stage.
2. Record the inclusion and exclusion criteria as Decisions and rules. Note the version date if criteria are amended.
3. Add verified sources to Links and listings as they are confirmed, with the decision they support (included, excluded, pending) and the reason.
4. Add tasks for each research phase: screening, extraction, quality assessment, synthesis. Note who is responsible and what decisions remain open.
5. Connect a supported AI app from Integrations. Ask it to read the project context and decisions at the start of each session so it starts from the current protocol state.