Search Articles

View query in Help articles search

Search Results (1 to 10 of 39 Results)

Download search results: CSV END BibTex RIS


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

For the patient-education bot, the following five criteria (maximum score: 15) were used: (1) medical information accuracy, (2) clarity and simplicity of explanations, (3) expressions of empathy and warmth, (4) explanation of purpose or importance of procedures, and (5) adherence to professional role boundaries.

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

JMIR Nursing 2025;8:e63058

Exploring Nursing Students’ Experiences of Empathy and User Experiences in an Immersive Virtual Reality Simulation Game: Cross-Sectional Study

Exploring Nursing Students’ Experiences of Empathy and User Experiences in an Immersive Virtual Reality Simulation Game: Cross-Sectional Study

Empathy is a key factor in quality of care and patient-centered care [1]. Greater empathy is associated with better clinical outcomes and patient-care experiences [2]. Empathy is an innate characteristic of the individual, and it is dynamic, constantly evolving, and built under the influence of various personal and environmental factors [3]. Empathy can be considered to include 3 dimensions: affective, cognitive, and behavioral [4,5].

Jaana-Maija Koivisto, Sanna Kämäräinen, Katri Mattsson, Satu Jumisko-Pyykkö, Riikka Ikonen, Elina Haavisto

JMIR Serious Games 2025;13:e62688

Impact of a Virtual Reality Intervention on Stigma, Empathy, and Attitudes Toward Patients With Psychotic Disorders Among Mental Health Care Professionals: Randomized Controlled Trial

Impact of a Virtual Reality Intervention on Stigma, Empathy, and Attitudes Toward Patients With Psychotic Disorders Among Mental Health Care Professionals: Randomized Controlled Trial

Of note, better attitudes and empathy of health care staff toward patients with psychotic disorders have been associated with better patient well-being, quality of patient care, and treatment outcomes [4-6]. In turn, attitude and empathy levels can also be influenced by the level of stigma [7]. Stigma has been commonly associated with psychotic disorders [8].

Jing Ling Tay, Yuanrong Qu, Lucas Lim, Rohan Puthran, Chye Lee Robert Tan, Rajkirren Rajendran, Ker Chiah Wei, Huiting Xie, Kang Sim

JMIR Ment Health 2025;12:e66925

Large Language Models and Empathy: Systematic Review

Large Language Models and Empathy: Systematic Review

Lee et al [38] used Chain-of-Empathy prompting to reason emotion and situational factors that may assist the model to infer the emotional experience. They evaluated GPT-3.5 and compared 4 unique prompts that used Chain-of-Empathy in generating empathetic responses to Reddit posts. The Chain-of-Empathy strategy resulted in improved the model’s empathy expression [38].

Vera Sorin, Dana Brin, Yiftach Barash, Eli Konen, Alexander Charney, Girish Nadkarni, Eyal Klang

J Med Internet Res 2024;26:e52597

Optimizing Compassion Training in Medical Trainees Using an Adjunct mHealth App: A Preliminary Single-Arm Feasibility and Acceptability Study

Optimizing Compassion Training in Medical Trainees Using an Adjunct mHealth App: A Preliminary Single-Arm Feasibility and Acceptability Study

Reference 1: A culture of compassion: how timeless principles of kindness and empathy become powerful Reference 8: A systematic review of educational interventions and their impact on empathy and compassionempathy

Jennalee S Wooldridge, Emily C Soriano, Gage Chu, Anaheed Shirazi, Desiree Shapiro, Marta Patterson, Hyun-Chung Kim, Matthew S Herbert

JMIR Form Res 2024;8:e60670

“Doctor ChatGPT, Can You Help Me?” The Patient’s Perspective: Cross-Sectional Study

“Doctor ChatGPT, Can You Help Me?” The Patient’s Perspective: Cross-Sectional Study

(G + H): Data collection: patients rated responses for empathy and usefulness, while physicians provided feedback encompassing empathy, usefulness, correctness, and potential harm. ENT: otolaryngology; EP: expert panel; GS: general surgery; Internal: internal medicine; Ped: pediatrics; trauma: traumatology. Two-sided t tests were used to compare 2 variables (eg, mean usefulness and empathy scores of responses of the EP with the ones of Chat GPT).

Jonas Armbruster, Florian Bussmann, Catharina Rothhaas, Nadine Titze, Paul Alfred Grützner, Holger Freischmidt

J Med Internet Res 2024;26:e58831

Empathy Toward Artificial Intelligence Versus Human Experiences and the Role of Transparency in Mental Health and Social Support Chatbot Design: Comparative Study

Empathy Toward Artificial Intelligence Versus Human Experiences and the Role of Transparency in Mental Health and Social Support Chatbot Design: Comparative Study

The study’s 4 comparisons are as follows: H-CR: we compared empathy toward the narrator across human-written retrieved stories and Chat GPT-retrieved stories H-CR+T: we compared empathy toward the narrator across human-written retrieved stories and Chat GPT-retrieved stories, making transparent to the user whether the story they read was written by a human or AI before they rated their empathy (repeat H-CR with transparency) H-CG: we compared empathy toward the narrator across human-written retrieved stories and

Jocelyn Shen, Daniella DiPaola, Safinah Ali, Maarten Sap, Hae Won Park, Cynthia Breazeal

JMIR Ment Health 2024;11:e62679

Empathic Conversational Agent Platform Designs and Their Evaluation in the Context of Mental Health: Systematic Review

Empathic Conversational Agent Platform Designs and Their Evaluation in the Context of Mental Health: Systematic Review

Only 5 (26%) of the 19 studies referred to an explicit definition of empathy, as summarized in Textbox 1. Studies and definition of empathy Jiang et al [21], 2022 Empathy processing is a situation-specific, cognitive-affective state or process with the projection of oneself into another’s feelings, actions, and experiences.

Ruvini Sanjeewa, Ravi Iyer, Pragalathan Apputhurai, Nilmini Wickramasinghe, Denny Meyer

JMIR Ment Health 2024;11:e58974

Performance of Large Language Models in Patient Complaint Resolution: Web-Based Cross-Sectional Survey

Performance of Large Language Models in Patient Complaint Resolution: Web-Based Cross-Sectional Survey

Responses were scored on four 10-point Likert scales for (1) appropriateness and completeness, (2) empathy, (3) satisfaction, and (4) an overall ranking item. The 10-point Likert scale ranged from 1=very poor, not empathetic, very dissatisfied to 10=very good, very empathetic, very satisfied. A 10-point Likert scale without a midpoint was chosen to avoid a neutral stand and allow an unequivocal selection [16].

Lorraine Pei Xian Yong, Joshua Yi Min Tung, Zi Yao Lee, Win Sen Kuan, Mui Teng Chua

J Med Internet Res 2024;26:e56413