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Skip search results from other journals and go to results- 25 Journal of Medical Internet Research
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Despite the dangers and increasing rates of online medicine purchases, there has been a scarcity of research to examine online medicine purchasing behavior. This paper examined online medicine purchasing through a behavioral model. The theory of planned behavior literature describes that past behavior, attitudes, perceived behavioral control (PBC), and norms affect a consumer’s intention and that this intention leads to a behavior.
JMIR Form Res 2025;9:e55391
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Digital Representation of Patients as Medical Digital Twins: Data-Centric Viewpoint
Much has been published about digital twins as a landmark of the digital transition of medicine and as a technology to address the uniqueness of patients in a precision medicine framework [1]. The digital twin concept combines engineering technologies attempting to represent objects digitally while maintaining a continuous connection with the physical object in the real world [2].
JMIR Med Inform 2025;13:e53542
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There are few prominent medicine-based national career development programs in the United States targeted to women, such as the Association of American Medical Colleges (AAMC) Early-Career and Mid-Career programs, the Hedwig van Ameringen Executive Leadership in Academic Medicine (ELAM) program, the University of Michigan’s Rudi Ansbacher Advancing Women in Academic Medicine Leadership Scholars Program, and Harvard’s Career Advancement and Leadership Skills for Women, with metrics demonstrating postparticipation
JMIR Form Res 2025;9:e65561
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Creating an ADR post classification model based on medical terminology from databases such as Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) or The Medical Dictionary for Regulatory Activities (Med DRA) creates a clinical foundation for future applications and can be used for various drugs and ADRs.
Using Chinese social media posts as the original dataset, we proposed a semisupervised learning framework for detecting Chinese drug terms and ADR terms [8].
JMIR Med Inform 2024;12:e45289
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Task-Specific Transformer-Based Language Models in Health Care: Scoping Review
Similarly, Locke et al [7] provided a comprehensive overview of NLP in medicine, emphasizing the potential of NLP technologies in transforming medical practice. Adyashreem et al [8] surveyed various NLP techniques in the biomedical field, shedding light on how these techniques can be applied to biomedical text for improved information extraction and analysis.
JMIR Med Inform 2024;12:e49724
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Prompt Engineering Paradigms for Medical Applications: Scoping Review
Reference 7: Large language models in medicine Case study in medicine Reference 29: Performance of ChatGPT incorporated chain-of-thought method in bilingual nuclear medicine 35: Diagnostic reasoning prompts reveal the potential for large language model interpretability in medicine Reference 108: MED-Prompt: a novel prompt engineering framework for medicine prediction on free-textmedicine
J Med Internet Res 2024;26:e60501
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Reference 9: The role of large language models in transforming emergency medicine: scoping review Reference 20: The intersection of ChatGPT, clinical medicine, and medical educationmedicine
JMIR Med Inform 2024;12:e59258
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The existing research on algorithmic bias has highlighted the importance of examining error rates, particularly in medicine where a false negative clinically translates to missed diagnoses or opportunities for treatment [3-6,26]. As described by Afrose and colleagues [26], focusing on global metrics of performance such as area under the receiver operating characteristic curve scores can neglect subtler disparities arising from differences in error rates affecting subgroups.
J Med Internet Res 2024;26:e46936
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Voluntary participation was sought from students in caring professions, encompassing education, medicine, nursing, and allied health, within a midsized research-intensive institution located in western Canada.
JMIR Nursing 2024;7:e53777
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The “Magical Theory” of AI in Medicine: Thematic Narrative Analysis
Participants were purposively sampled and came from various disciplines and backgrounds: medicine, bioethics, public health, philosophy, psychology, economy, law, and computer science. Inclusion criteria, other than being exposed to medical AI in their profession, were the holding of a senior position, either in academia or in the private sector, hence excluding Ph D students, interns, and junior professionals.
JMIR AI 2024;3:e49795
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