Data-reflector: an agentic exploratory data analysis platform for researchers.

OBJECTIVES

Exploratory data analysis (EDA) is foundational to clinical research yet inaccessible to clinician-scientists without programming training. Existing large language model (LLM) tools improve accessibility but struggle with hallucinations. We developed and evaluated data reflector (DR), a hybrid agentic platform pairing natural language interaction with deterministic statistical computation.

MATERIALS AND

METHODS

Data reflector restricts the LLM to intent interpretation, tool selection, and filter parsing while routing all numerical computation through predefined deterministic functions executed locally. We piloted DR with 6 participants of diverse technical backgrounds across 6 clinical (MIMIC-IV, eICU Collaborative Research Database, National Health and Nutrition Examination Survey) and nonclinical (energy, COVID-19, urban mobility) datasets, assessing usability (system usability scale, SUS), task completion time, analytical output correctness against participant-generated manual reference results, hallucination prevention, and independent reproducibility by a second operator. A head-to-head comparison with ChatGPT 5.3 chatbot was also conducted.

RESULTS

Data reflector achieved a mean SUS of 94.58, with reduced task completion time across all evaluable participants. Statistical outputs and generated distribution curves demonstrated complete agreement with participant-generated manual reference results across all evaluable outputs. Independent reexecution by a different operator using independently constructed prompts also demonstrated complete agreement with the original DR outputs across all evaluated tasks. Hallucination-prevention safeguards correctly declined out-of-scope and impossible queries. ChatGPT produced incomplete, internally inconsistent outputs on identical tasks where DR returned complete, accurate results.

DISCUSSION: Data reflector's architectural separation of LLM reasoning from numerical computation removes dependence on prompt phrasing, session history, and stochastic LLM sampling while preserving conversational accessibility, addressing reproducibility limitations of general-purpose LLM tools.

CONCLUSION

Data reflector offers a practical pattern for reproducible, accessible artificial intelligence-assisted EDA across heterogeneous data sources in modern clinical and translational research.