Trustworthy Agentic AI for Healthcare — research prototype

A repository-documented NHANES-based research prototype combining diabetes-related early-risk modelling, explanations, trust and fairness evaluation, safety gates and a multi-agent workflow. The README describes a locked logistic-regression model and a Streamlit demonstrator.

Workflow level n/a Source Repository-documented Outcome Not empirically evaluated here

Evidence & verification

Source level

Repository-documented
Grounded in a maintainer repository/README. Availability, stars and forks do not establish research benefit or correctness.

Outcome verification

Not empirically evaluated here
A documented resource or intended workflow, not a demonstrated academic outcome. No empirical benefit is established by its inclusion.

Sources checked

2026-10-17 (UTC)

README supports NHANES, logistic regression and the described components. Its nine-stage diagram labels End-to-End Validation but does not establish an independently validated result. No performance or safety guarantee inferred; not-evaluated concerns editorial outcome evidence, not absence of author development.

Source-checked does not mean independently reproduced. See the Method page for how these labels are assigned.

Sources (1)

Research applicability

Getting started

Editorial suggestion, not executed: read the notebook sequence and data provenance notes, then review the explanation method and safety gates using non-sensitive examples before considering local experiments.

Maintenance

Current README/abstract access is not a maintenance or deployment audit.

License

Check original code/document and data terms before reuse; license file not audited.

Workflow & human oversight

Resource-level automation is unspecified; researchers select tasks, review sources, validate outputs and authorize consequential actions.

Workflow level is not an evidence score; higher is not better evidence.

Approach

Limitations

README-only evidence; notebooks, model artifacts, clinical validity, fairness and deployed application were not independently evaluated. The project explicitly disclaims diagnosis and treatment recommendations. SHAP exploration and coefficient-based demonstrator explanations are different methods and must not be conflated. This is not a clinically validated system; individual risk calibration, safety and fairness are not established.

Reproducibility

Primary text inspected only; no code, data or outcomes reproduced.

Adoption requirements

Inspect the NHANES source/processing notes, model artifacts and demonstrator dependencies before reuse; repository documentation alone does not establish a safe clinical setup. Respect public-use data conditions and avoid uploading private health records; qualified clinical oversight is required for any interpretation.

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