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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].
JMIR Med Educ 2025;11:e73698
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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].
JMIR Med Educ 2025;11:e72998
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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].
JMIR Form Res 2025;9:e64266
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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].
JMIR Rehabil Assist Technol 2025;12:e63235
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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.
JMIR Med Educ 2025;11:e63400
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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.
JMIR Aging 2025;8:e66778
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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].
J Med Internet Res 2025;27:e60948
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We predicted that (1) both Latinx and White adolescents would report higher preferences, including more positive attitudes (advertisement likeability; positive affect; and brand perceptions) and behavioral intentions (product consumption and social media engagement) when advertisements featured Latinx celebrities versus Latinx noncelebrities; (2) Latinx adolescents would report higher preferences for all advertisements compared to White adolescents; and (3) Latinx adolescents would show a stronger positive response
J Med Internet Res 2025;27:e53188
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Reference 40: Attitudes and perceptions toward healthcare technology adoption among older adults in singaporeperceptions
JMIR Form Res 2025;9:e63568
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Much of the literature on technology-based interventions involves randomized controlled trials examining postconcussion symptom improvement after interventions; however, in addition to quantitative measures, understanding the perceptions and acceptance of self-management tools is essential to identify barriers and facilitators to adherence and effectiveness of these tools.
JMIR Rehabil Assist Technol 2025;12:e59181
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