Clinical Research Directory

Effectiveness of Chatbot-Supported Web Program in Postpartum Care

The purpose of this clinical study was to evaluate the effectiveness of a web-based "Postpartum Care Package" program developed for postpartum women and delivered via a chatbot, and to examine its effects on the postpartum health of mothers and newborns. The key questions it aims to answer are: * What is the impact of the chatbot-supported web-based "Postpartum Care Package" program on the postpartum health of mothers and newborns? * Does the chatbot-supported web-based "Postpartum Care Package" program increase women's accessibility and availability of postpartum care resources?

Effect of AI-Supported Virtual Patient Chatbot Training on Nursing Students' Self-Perceived Clinical Decision-Making and Gynecological Cancer Knowledge

This randomized controlled trial evaluated the effects of structured case-based training delivered through an artificial intelligence-supported educational virtual patient chatbot platform on nursing students' gynecological cancer knowledge and self-perceived clinical decision-making. Third-year undergraduate nursing students who had completed the Obstetrics and Gynecology Nursing course were randomly assigned in a 1:1 ratio to an intervention group or an assessment-only wait-list control group. The intervention group completed five AI-supported virtual patient training scenarios during a two-week access period, followed by a common virtual patient assessment. The control group completed the same virtual patient assessment without access to the training scenarios during the study assessment period. Knowledge and self-perceived clinical decision-making were assessed at baseline and immediately after the assigned study procedures. Virtual patient performance was assessed once at the final assessment.

DialysisBot: AI Chatbot for Hemodialysis Self-Management

End-stage kidney disease requiring hemodialysis is a chronic condition with substantial clinical, functional, and psychosocial burden. Maintaining an adequate health status between dialysis sessions depends largely on patients' ability to self-manage key aspects of treatment, including fluid restriction, dietary control, and vascular access care. Adherence in this population remains frequently suboptimal, and elevated interdialytic weight gain (IDWG), hyperphosphatemia, and hyperkalemia are among the most common complications associated with non-adherence. DialysisBot is a multiplatform, artificial intelligence-based conversational agent designed to support hemodialysis patients in the day-to-day self-management of their disease. The system combines a generative large language model for natural dialogue management, a fine-tuned BERT classifier for recognizing the domain of each patient request (diet, fluid intake, vascular access care, or organizational aspects of dialysis), and a vector-similarity retrieval engine that limits every response to a clinically validated knowledge base preloaded by the research team and approved by the hospital institution. The system performs no autonomous web search. When the cosine similarity between a user query and the indexed reference documents falls below a predefined threshold, the system withholds a clinical answer and instead informs the patient that it cannot respond, directing them to contact healthcare staff; this mechanism is the main technical safeguard against hallucinated or unvalidated responses. This is a prospective experimental pilot study evaluating the feasibility, acceptability, impact, and user satisfaction associated with DialysisBot as a self-management support tool for patients undergoing hemodialysis. Secondary objectives include assessing support for dietary management (phosphorus, potassium, and sodium restriction), support for interdialytic fluid intake control, whether system-provided guidance on vascular access management (arteriovenous fistula and central venous catheter) is put into practice and consistent with current standards of care, the impact on treatment adherence, and the barriers, facilitators, and overall user experience associated with the intervention. The study is organized into two methodological phases: a quantitative longitudinal phase (T0 baseline, T1 at 1 month, T2 at 3 months) using standardized patient-reported outcome measures and routine clinical parameters, followed by a qualitative phase conducted after T2, based on semi-structured interviews analyzed through reflexive thematic analysis. A convenience sample of 20-40 adult patients undergoing chronic hemodialysis, recruited through a participating dialysis center and/or an online patient community, will be enrolled and trained on the application before use. As a pilot feasibility study, its results are intended to inform the design and sample size of future, larger-scale confirmatory trials.

A Mobile Chatbot with ARCS-V Motivation Theory on Learning Motivation

This study will implement a smartphone chatbot in medical-surgical nursing education to enhance nursing students' learning motivation and academic performance. T