Search Articles

View query in Help articles search

Search Results (1 to 10 of 90 Results)

Download search results: CSV END BibTex RIS


Patients’ Perceptions of Artificial Intelligence Acceptance, Challenges, and Use in Medical Care: Qualitative Study

Patients’ Perceptions of Artificial Intelligence Acceptance, Challenges, and Use in Medical Care: Qualitative Study

A qualitative methodology allows us to gather participants’ perceptions in an unbiased way, and especially to recognize the reasons for these perceptions to enhance understanding [26,27]. The exchange in focus groups and the stimulus given can trigger responses and allow participants to build on ideas that might not have come up in individual interviews [28].

Jana Gundlack, Carolin Thiel, Sarah Negash, Charlotte Buch, Timo Apfelbacher, Kathleen Denny, Jan Christoph, Rafael Mikolajczyk, Susanne Unverzagt, Thomas Frese

J Med Internet Res 2025;27:e70487

COVID-19 Perceptions Among Communities Living on Ground Crossings of Somali Region of Ethiopia: Community Cross-Sectional Survey Study

COVID-19 Perceptions Among Communities Living on Ground Crossings of Somali Region of Ethiopia: Community Cross-Sectional Survey Study

Beliefs and perceptions of the virus’s spread and control were partially adapted from the World Health Organization (WHO) resources. Specifically, some of the questions were adapted from the COVID-19 rapid quantitative assessment tool [8,9]. Three main perception themes were explored: perceived facilitators for the spread of the virus (6 items), perceived inhibitors (7 items), and risk labeling (8 items), as well as sociodemographic variables, including access to communication resources.

Alinoor Mohamed Farah, Abdifatah Abdulahi, Abdulahi Hussein, Ahmed Abdikadir Hussein, Abdi Osman, Mohamed Mohamud, Hasan Mowlid, Girum Hailu, Fathia Alwan, Ermiyas Abebe Bizuneh, Ahmed Mohammed Ibrahim, Elyas Abdulahi

JMIR Form Res 2025;9:e66751

Authors’ Reply: Citation Accuracy Challenges Posed by Large Language Models

Authors’ Reply: Citation Accuracy Challenges Posed by Large Language Models

We appreciate the thoughtful critique of our manuscript “Perceptions and earliest experiences of medical students and faculty with Chat GPT in medical education: qualitative study” [1] by Zhao and Zhang [2]. Concerns over the generation of hallucinated citations by large language models (LLMs), such as Open AI’s Chat GPT, Google’s Gemini, and Hangzhou’s Deep Seek, warrant exploring advanced and novel methodologies to ensure citation accuracy and overall output integrity [3].

Mohamad-Hani Temsah, Ayman Al-Eyadhy, Amr Jamal, Khalid Alhasan, Khalid H Malki

JMIR Med Educ 2025;11:e73698

Citation Accuracy Challenges Posed by Large Language Models

Citation Accuracy Challenges Posed by Large Language Models

In the recent study titled “Perceptions and earliest experiences of medical students and faculty with Chat GPT in medical education: qualitative study,” the section addressing concerns about Chat GPT deserves a deeper discussion [1]. There are several reasons for the citation issues in LLMs, which can be analyzed as follows. First, most LLMs cannot access paid subscription databases and therefore solely rely on open-access resources [2].

Manlin Zhang, Tianyu Zhao

JMIR Med Educ 2025;11:e72998

Perceived Trust and Professional Identity Threat in AI-Based Clinical Decision Support Systems: Scenario-Based Experimental Study on AI Process Design Features

Perceived Trust and Professional Identity Threat in AI-Based Clinical Decision Support Systems: Scenario-Based Experimental Study on AI Process Design Features

Advanced medical students, therefore, are well-positioned to recognize and articulate perceptions of professional identity threats. For the study, 8 different scenarios of a fictitious AI-based CDSS were developed, which showed the user the risk of developing sepsis within the next 48 hours. Sepsis is a complex infectious disease that can develop from any focus of infection. It can be caused by viruses, bacteria, fungi, or parasites and can affect patients of all ages [55].

Sophia Ackerhans, Kai Wehkamp, Rainer Petzina, Daniel Dumitrescu, Carsten Schultz

JMIR Form Res 2025;9:e64266

Speech and Language Therapists’ Perspectives of Virtual Reality as a Clinical Tool for Autism: Cross-Sectional Survey

Speech and Language Therapists’ Perspectives of Virtual Reality as a Clinical Tool for Autism: Cross-Sectional Survey

The precursor to using VR in clinical practice is exploring speech and language therapists’ perceptions of VR and appetite for future usage. This may help to bridge the gap between technology and practice. Studies documenting clinician perceptions of VR in adult rehabilitation are positive, but knowledge and attitudes toward VR act as barriers to adoption [19].

Jodie Mills, Orla Duffy

JMIR Rehabil Assist Technol 2025;12:e63235

Perceptions and Earliest Experiences of Medical Students and Faculty With ChatGPT in Medical Education: Qualitative Study

Perceptions and Earliest Experiences of Medical Students and Faculty With ChatGPT in Medical Education: Qualitative Study

However, little is known specifically about the perceptions and experiences of faculty and students or trainees against the use of Chat GPT in the context of medical education within Saudi Arabia. The health care sector in Saudi Arabia is experiencing dramatic growth and reformatting, with a strong emphasis on prioritizing medical education and digitizing the health systems.

Noura Abouammoh, Khalid Alhasan, Fadi Aljamaan, Rupesh Raina, Khalid H Malki, Ibraheem Altamimi, Ruaim Muaygil, Hayfaa Wahabi, Amr Jamal, Ali Alhaboob, Rasha Assad Assiri, Jaffar A Al-Tawfiq, Ayman Al-Eyadhy, Mona Soliman, Mohamad-Hani Temsah

JMIR Med Educ 2025;11:e63400

Exploring Older Adults’ Perspectives and Acceptance of AI-Driven Health Technologies: Qualitative Study

Exploring Older Adults’ Perspectives and Acceptance of AI-Driven Health Technologies: Qualitative Study

This approach helps in the effort to uncover and understand the experiences, attitudes, and perceptions of people, making it appropriate for this study. The findings from a qualitative descriptive study are able to inform strategies that promote and facilitate the use of AI-based health technologies, which makes it particularly useful for this research.

Arkers Kwan Ching Wong, Jessica Hiu Toon Lee, Yue Zhao, Qi Lu, Shulan Yang, Vivian Chi Ching Hui

JMIR Aging 2025;8:e66778

Perceptions in 3.6 Million Web-Based Posts of Online Communities on the Use of Cancer Immunotherapy: Data Mining Using BERTopic

Perceptions in 3.6 Million Web-Based Posts of Online Communities on the Use of Cancer Immunotherapy: Data Mining Using BERTopic

Patients’ decisions to undergo immunotherapy are influenced by a range of factors, including their perceptions of its efficacy, side effects, procedural aspects, costs, their levels of knowledge about the treatment, and the comprehensiveness of advice provided by health care providers [5,6].

Xingyue Wu, Chun Sing Lam, Ka Ho Hui, Herbert Ho-fung Loong, Keary Rui Zhou, Chun-Kit Ngan, Yin Ting Cheung

J Med Internet Res 2025;27:e60948