Clinical Research Directory

Safety, Feasibility, and Acceptability of the MINERVA Voice Conversational Agent for Emergency Department History-Taking

This study evaluates whether an artificial intelligence (AI) conversational agent called MINERVA (Medical Interface for Navigating the Emergency Room with Voice Assistance) can safely and feasibly gather medical histories from emergency department (ED) patients and produce accurate summaries. The main questions it aims to answer are: Can an AI conversational agent gather and summarize patient histories in the emergency department? Are the resulting summaries accurate? Do they capture essential clinical information? Can an AI agent overseeing these conversations independently identify red-flag symptoms that may need time-sensitive clinical interventions? Adult ED patients interact with MINERVA via either voice chat or typing on a keyboard. MINERVA asks them questions about their symptoms and generates a summary which the patients review for accuracy. MINERVA reviews the conversation for clinical red flags that suggest urgent evaluation is required. Participants fill out a survey providing feedback on their experience. The summary is not saved in the medical record and clinical care is not affected. Physician reviewers will evaluate the safety, accuracy, and clinical value of the MINERVA interview transcripts and summaries using standardized rubrics, including whether MINERVA was correct in identifying red-flag symptoms that may need time-sensitive clinical interventions.

Intelligent Simulated Patient System for Gastroenterology Education

This randomized controlled trial evaluated a large language model-based Intelligent Simulated Patient System (ISPS) for gastroenterology history-taking education. Ninety medical students were randomly assigned in a 1:1 ratio to 4 weeks of conventional clinical learning or ISPS-assisted training. The ISPS used teacher-defined structured case information to support simulated patient interactions and generated item-level formative feedback using predefined scoring rubrics. The implemented system did not use external electronic health record retrieval, retrieval-augmented generation, a vector database, or semantic-similarity threshold scoring. The primary outcomes were post-intervention medical history-taking performance and clinical diagnostic accuracy. History-taking performance was assessed in a standardized Objective Structured Clinical Examination by two senior physicians who were blinded to group allocation. Secondary outcomes included patient-centered communication competence and participants' acceptance of the ISPS.

AI Virtual Patient Training for Dental Student History Taking

This randomized study evaluated whether an artificial intelligence-assisted virtual patient could support third-year dental students in practicing medical and dental history taking. Fifty-six students were assigned to either a Gemini-based virtual-patient training group or a conventional role-play group. Both groups completed four training sessions over 2 weeks using comparable clinical cases, practice time, and feedback criteria. The study compared changes in case-based history-taking competency as well as student satisfaction and perceived usefulness of the training methods.