Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Introduction to Language of Pathophysiology ll

Introduction to Language of Pathophysiology ll

This lesson explores key terms that describe how diseases progress, their outcomes, and their distribution in populations.Diagnostic tests identify diseases and monitor treatment. These include blood and urine tests, biopsies, imaging (X-ray, MRI), and detection of infectious agents.Remission is a reduction or disappearance of symptoms.Exacerbation refers to the worsening of symptoms, such as increased wheezing during an asthma attack.A precipitating factor triggers an acute episode, while a...

Methods of Documentation VI: Case Management Model

Methods of Documentation VI: Case Management Model

The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
For example, a patient with a chronic illness...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Cross-Cultural Adaptation and Validation of the Japanese Charité Alarm Fatigue Questionnaire (CAFQa) Among ICU Nurses and Physicians: A Multicentre Study.

Nursing in critical care·2026

Same author

Anticoagulation therapy for Lemierre syndrome: a comprehensive review and individual participant data analysis.

Thrombosis journal·2026

Same author

Lateral cutaneous nerve entrapment syndrome associated with appendicitis.

Journal of surgical case reports·2026

Same author

Direct Oral Anticoagulant Consumption and the Risk of Acute Interstitial Lung Diseases: A Case-Crossover Study Using Japanese Administrative Data.

Pharmacoepidemiology and drug safety·2026

Same author

Unfilled Prescriptions in Japan: A 10-Year Retrospective Analysis.

Pharmacoepidemiology and drug safety·2026

Same author

Effectiveness of a Fully Online Scientific Research Works Peer Support Group Model for Research Capacity Building Through Conducting Systematic Reviews Among Health Care Professionals: Retrospective Cohort Studies.

JMIR medical education·2025

Search research articles

Related Experiment Video

Updated: Jun 17, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Developing a Large Language Model-Based Feedback System for Case Report Writing in Rehabilitation Education:

Yuuto Tonouchi¹, Shunsuke Nakai^2,3, Kayo Murakami¹

¹Department of Rehabilitation, Kyoto Min-iren Asukai Hospital, Kyoto, Kyoto, Japan.

JMIR Medical Education

|June 15, 2026

Summary

This summary is machine-generated.

This tutorial guides educators in building an AI feedback system for novice healthcare staff using accessible tools. The system enhances clinical reasoning and provides efficient feedback while ensuring data privacy.

Keywords:

AI artificial intelligence case report education feedback large language model rehabilitation

Related Experiment Videos

Last Updated: Jun 17, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

Medical Education Technology
Artificial Intelligence in Healthcare
Clinical Training Systems

Background:

Novice healthcare staff require structured feedback for case reports during clinical training.
Existing feedback systems face challenges due to time constraints and instructor shortages.
Large language models (AI) present opportunities to improve educational feedback in clinical settings.

Purpose of the Study:

To provide a practical guide for educators to develop an AI-based feedback system without programming experience.
To demonstrate the use of Dify, Slack, and Google Apps Script for creating an AI feedback tool.
To balance educational quality, operational efficiency, and data privacy in clinical education.

Main Methods:

Developed a feedback system with four AI chatbots: loop-based for clinical reasoning and single-shot for proofreading/summarization.
Outlined system architecture, feedback design based on formative assessment, and implementation steps.
Incorporated data privacy safeguards, including deidentification and API-based protection, with a pilot implementation.

Main Results:

Pilot implementation confirmed the feasibility of deploying and operating the AI feedback system within clinical education workflows.
Participant feedback highlighted high usability and the effectiveness of iterative feedback in engaging learners.
Identified areas for refining feedback criteria to align with institutional educational goals.

Conclusions:

Presents a reproducible framework for creating customized AI feedback systems with human oversight and robust data privacy.
Enables educators to implement adaptive systems tailored to specific institutional contexts and clinical domains.
Facilitates enhanced clinical reasoning and efficient feedback delivery for novice healthcare professionals.