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Investigating Placebos and Controls Used in Large Language Model-Based Chatbot Intervention Trials: Protocol for a

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This summary is machine-generated.

This systematic review will map control strategies in large language model (LLM) chatbot trials for digital health. It aims to improve comparator selection for more accurate and reproducible patient-facing LLM intervention studies.

Keywords:
chatbotscontrol conditionsdigital healthlarge language modelsmethodological review

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Area of Science:

  • Digital Health
  • Artificial Intelligence
  • Clinical Trials

Background:

  • Large language model (LLM)-based chatbots are increasingly used as patient-facing digital health tools.
  • Their engaging nature complicates causal inference due to expectancy-nonspecific factors.
  • Inconsistent and undermatched comparator strategies in LLM trials risk biased results and poor reproducibility.

Purpose of the Study:

  • To systematically identify and categorize control conditions in LLM-based patient-facing digital health intervention studies.
  • To evaluate the methodological appropriateness of these control conditions.
  • To explore variations by health domain and study design, and the relationship between control type/quality and reported effects.

Main Methods:

  • Protocol follows PRISMA-P guidelines and is registered with PROSPERO.
  • Eligible studies include interventional designs of LLM-based patient-facing digital health interventions with any control type.
  • Searches will be conducted across major databases (PubMed, PsycINFO, CENTRAL, CINAHL, Scopus) from January 1, 2023, with dual independent screening and data extraction.

Main Results:

  • Scoping searches are complete; full screening and data extraction are pending.
  • The protocol is registered in PROSPERO (CRD420251246148).
  • No specific funding has been received at the time of submission.

Conclusions:

  • This review will empirically map control practices in LLM chatbot trials.
  • It will provide guidance for designing better-matched comparators.
  • This supports more valid and interpretable evaluations as LLMs are adopted in patient care.