Q: What are AI guardrails for clinical research and why are they important? A: AI guardrails for clinical research are practices like locked study protocols, explicit causal models, transparent code, provenance logs, and persistent human oversight designed to protect causal logic, bias control, and reproducibility. They are important because the study shows AI tools can produce plausible-looking but incorrect or unstable results that skip causal reasoning, misinterpret metrics, and undermine reproducibility. Q: What methodological failures did the study observe when using AI tools for causal analysis? A: The study found that AI-generated analyses skipped theoretical causal modeling, omitted formal adjustment sets, misinterpreted an odds ratio as a direct change in probability, and produced inconsistent results when the same prompt was re-run. Expert-guided prompts also produced a conceptually meaningless DAG that was not integrated into the analysis, and one run failed due to a data-type conversion error. Q: How should research teams decide the appropriate level of AI automation? A: The article outlines a five-tier automation hierarchy and recommends choosing the lowest level that meets safety needs, with Levels 1–3 (basic assistance to managed execution) usually appropriate for causal studies. Level 4 should be reserved for mature, well-validated pipelines with strong audits and Level 5 full autonomy should be avoided for high-stakes health research today. Q: What practical actions does the playbook recommend to protect study validity when using AI? A: The playbook lists six actions: write and lock the study protocol; make the causal model explicit with a DAG and justified adjustment set; keep a human in the loop to review and accept code and outputs; track provenance by logging prompts, model versions, and data snapshots; validate data and model assumptions and run sensitivity checks; and report clearly, separating prediction from causation and sharing code where possible. Following these steps helps make AI-assisted workflows visible, testable, and reproducible. Q: What red flags should teams watch for that suggest AI outputs may be unreliable? A: Key red flags include AI outputs that skip the causal model or fail to state an adjustment set, interpretations that confuse odds, risk, and probability, and repeated runs that yield different estimates without explanation. Also watch for polished DAGs or plots that contradict domain knowledge and for silent data errors such as type casts or encoding issues that break steps. Q: How can researchers ensure reproducibility when incorporating AI into analyses? A: Researchers should publish or archive analysis plans, code, and prompts, use version control for data and models, and record random seeds and environments so analyses can be rerun exactly. They should also re-run analyses from a clean start, compare outputs across small prompt changes, and use simulations or negative controls to test false-positive risks. Q: How should causal reasoning be integrated into AI-assisted health research workflows? A: Integrate causal reasoning by explicitly drawing a DAG, justifying the adjustment set, and ensuring subsequent analyses follow that causal model rather than relying solely on predictive features. Experts must review the DAG and confirm the AI’s analytical steps align with the prespecified protocol to prevent drift from causal inference to correlation. Q: What role does human accountability play in safe AI use for clinical studies? A: Human accountability means maintaining a persistent human-in-the-loop who peer-reviews, rejects, revises, and accepts text and code, aligns the AI’s role with workflow boundaries, and enforces error tolerance and epistemic responsibility. The article stresses that keeping humans accountable, along with transparent logs and enforced AI guardrails for clinical research, is essential to preserve scientific and clinical integrity when using AI in health research.