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Skip search results from other journals and go to results- 97 Journal of Medical Internet Research
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A novel 7-point scale was developed following a pilot test conducted by two of the authors to address the complexities of comparing codes generated by humans and Chat GPT. This scale, presented in the first column of Table 2, focuses on exploring the complementary and divergent insights between human-generated and Chat GPT-generated codes.
JMIR AI 2025;4:e64447
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Unlike the template-based method, this method uses the ability of artificial intelligence (AI) to generate content dynamically, for example, using Chat GPT, which is an AI-based chatbot developed by Open AI, for creating items based on specific topics or learning outcomes provided by users [17-21]. This approach allows for the generation of diverse and complex questions in seconds, offering flexibility and efficiency in item development.
JMIR Form Res 2025;9:e65726
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Recently, large language models (LLMs) such as Chat GPT [12] and GPT-4 [13] have captured worldwide attention due to their astonishing text-generation capabilities. Through pretraining on vast amounts of data, LLMs demonstrate remarkable performance on unseen downstream tasks using zero-shot, one-shot, or few-shot prompts without parameter updates [14].
J Med Internet Res 2025;27:e65547
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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
Large language models (LLMs) such as Deep Seek, Chat GPT, and Chat GLM have significant limitations in generating citations, raising concerns about the quality and reliability of academic research. These models tend to produce citations that are correctly formatted but fictional in content, misleading users and undermining academic rigor.
JMIR Med Educ 2025;11:e72998
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Juels Parker commented on our study comparing the sufficiency of Chat GPT, Google Bard, and Bing artificial intelligence (AI) in generating patient-facing responses to questions about five dermatological diagnoses [1,2]. He highlights an important need to compare AI to existing patient education tools, such as handouts, peer-reviewed articles, and patient-centered websites.
We agree that AI is not a benign entity, and many resources exist for patients to learn about their conditions, aside from AI [3,4].
JMIR Dermatol 2025;8:e72540
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Reference 1: The comparative sufficiency of ChatGPT, Google Bard, and Bing AI in answering diagnosis,chatgptTheme Issue (2023): AI and ChatGPT in Dermatology
JMIR Dermatol 2025;8:e71768
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The doctors’ responses and Chat GPT responses for the same question were anonymized and randomly labeled as Response 1 and Response 2.
J Med Internet Res 2025;27:e62857
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There has a been rapid popularization of the LLM, Chat GPT for home-based medical inquiries [3]. Minimal research exists on Chat GPT’s accuracy in detecting melanoma. Given that patients are increasingly presenting internet-derived diagnostics during cancer consultations, it is imperative to understand the capabilities of commonly used AI engines, such as Chat GPT [4].
JMIR Dermatol 2025;8:e67551
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To determine the extent of exposure to Chat GPT, for each participant in MIBot (version 5.2 A), we included an additional short survey in the 1-week-later survey referred to as the Chat GPT survey. It contained 8 new questions designed to evaluate the participant’s knowledge and use of Chat GPT prior to engaging in MIBot (version 5.2 A). The full Chat GPT survey can be found in Multimedia Appendix 1.
JMIR Form Res 2025;9:e56973
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