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Advancing Clinical Chatbot Validation Using AI-Powered Evaluation With a New 3-Bot Evaluation System: Instrument Validation Study

Advancing Clinical Chatbot Validation Using AI-Powered Evaluation With a New 3-Bot Evaluation System: Instrument Validation Study

The following guidelines were incorporated into its prompt: (1) adopt a teaching role tailored to patients with limited medical knowledge; (2) provide accurate, comprehensive explanations of medical terms and procedures in simple, relatable language; (3) exhibit empathy and warmth while refraining from making medical recommendations outside the scope of a patient-education nurse; and (4) ensure consistency in tone and responsiveness to patient questions while maintaining a clear boundary of professional role

Seungheon Choo, Suyoung Yoo, Kumiko Endo, Bao Truong, Meong Hi Son

JMIR Nursing 2025;8:e63058

Facilitating Trust Calibration in Artificial Intelligence–Driven Diagnostic Decision Support Systems for Determining Physicians’ Diagnostic Accuracy: Quasi-Experimental Study

Facilitating Trust Calibration in Artificial Intelligence–Driven Diagnostic Decision Support Systems for Determining Physicians’ Diagnostic Accuracy: Quasi-Experimental Study

The implementation of artificial intelligence (AI)–driven automated medical history–taking systems with differential diagnosis generators is a promising solution, as these systems provide a list of potential differential diagnoses before the information is collected by physicians, thereby aiding more accurate diagnoses [4]. However, AI-related diagnostic errors have become a problem [5].

Tetsu Sakamoto, Yukinori Harada, Taro Shimizu

JMIR Form Res 2024;8:e58666

Accuracy of a Commercial Large Language Model (ChatGPT) to Perform Disaster Triage of Simulated Patients Using the Simple Triage and Rapid Treatment (START) Protocol: Gage Repeatability and Reproducibility Study

Accuracy of a Commercial Large Language Model (ChatGPT) to Perform Disaster Triage of Simulated Patients Using the Simple Triage and Rapid Treatment (START) Protocol: Gage Repeatability and Reproducibility Study

However, at present, it is unclear whether LLMs can provide sufficiently accurate triage for use in a disaster. Presently, the Simple Triage and Rapid Treatment (START) algorithm, which divides disaster casualties into four different triage codes, is the most commonly used model for disaster triage [5]. Disaster casualties are classified into four groups that are named by priority and color: red (immediate), yellow (delayed), green (ambulatory), and black (expectant) [5].

Jeffrey Micheal Franc, Attila Julius Hertelendy, Lenard Cheng, Ryan Hata, Manuela Verde

J Med Internet Res 2024;26:e55648

Blood Pressure Measurement Based on the Camera and Inertial Measurement Unit of a Smartphone: Instrument Validation Study

Blood Pressure Measurement Based on the Camera and Inertial Measurement Unit of a Smartphone: Instrument Validation Study

There will also be issues about the necessity of regular calibration and how often those calibrations will be needed for accurate blood pressure measurements. Ultimately, a calibration-free system is needed, but the development of calibration-free devices is still ongoing [24]. To minimize the calibration issues, new devices that can be used without calibration should be developed.

Yong-Hoon Yoon, Jongin Kim, Kwang Jin Lee, Dongrae Cho, Jin Kyung Oh, Minsu Kim, Jae-Hyung Roh, Hyun Woong Park, Jae-Hwan Lee

JMIR Mhealth Uhealth 2023;11:e44147