JAMIA open · 2026
Data-reflector: an agentic exploratory data analysis platform for researchers.
СтатьяНазвание и аннотация: Перевод в очереди
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Sabounchi Moein, Hazare Nimay, Capone Chris, Jangda Mateen, Oh Wonsuk, Jayaraman Pushkala, Gupta Shashank, Sawant Ashwin, Kuschner Zachary, Parchure Prathamesh, Timsina Prem, Kaplan Ben, Vakil Aditi, Kohli-Seth Roopa, Kovatch Patricia, Zebrowski Alexis, Glicksberg Benjamin, Agrawal Pulkit, Darrow Bruce, Stump Lisa, Charney Alexander, Nadkarni Girish N, Sakhuja Ankit
Аннотация
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.
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.