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Diagnostic Prediction Models for Primary Care, Based on AI and Electronic Health Records: Systematic Review

Diagnostic Prediction Models for Primary Care, Based on AI and Electronic Health Records: Systematic Review

Applicability concern was rated based on 3 domains (participants, predictors, and outcome) and an overall judgment of applicability (ie, low, unclear, or high) was also given with the same approach as the risk-of-bias scoring. Applicability evaluation depends on the review question [69], and we translated applicability assessment as usability of the diagnostic prediction model in a PC setting.

Liesbeth Hunik, Asma Chaabouni, Twan van Laarhoven, Tim C Olde Hartman, Ralph T H Leijenaar, Jochen W L Cals, Annemarie A Uijen, Henk J Schers

JMIR Med Inform 2025;13:e62862


A Novel Framework to Assess Clinical Information in Digital Health Technologies: Cross-Sectional Survey Study

A Novel Framework to Assess Clinical Information in Digital Health Technologies: Cross-Sectional Survey Study

However, these approaches addressed only the content and face validity of the CLIQ framework and not its applicability, reliability, and construct validity. Therefore, the aim of this study is to assess the applicability, internal consistency, and construct validity of the CLIQ framework. Clinical Information quality framework for digital health technologies. Stages of CLIQ framework Development.CLIQ: Clinical Information Quality.

Kayode Philip Fadahunsi, Petra A Wark, Nikolaos Mastellos, Ana Luisa Neves, Joseph Gallagher, Azeem Majeed, Josip Car

JMIR Med Inform 2025;13:e58125


Ensuring General Data Protection Regulation Compliance and Security in a Clinical Data Warehouse From a University Hospital: Implementation Study

Ensuring General Data Protection Regulation Compliance and Security in a Clinical Data Warehouse From a University Hospital: Implementation Study

The objective of this study is to assess the applicability of this pioneering framework. The evaluation criterion is the level of compliance of the e HOP CDW with the CNIL CDW framework. For requirements where compliance proves challenging, we propose adjustments for consideration by national authorities. This paper first outlines the defining features of CDWs and the regulatory requirements stemming from GDPR and the French Data Protection Act.

Christine Riou, Mohamed El Azzouzi, Anne Hespel, Emeric Guillou, Gouenou Coatrieux, Marc Cuggia

JMIR Med Inform 2025;13:e63754


Author’s Reply: Examining Multimodal AI Resources in Medical Education: The Role of Immersion, Motivation, and Fidelity in AI Narrative Learning

Author’s Reply: Examining Multimodal AI Resources in Medical Education: The Role of Immersion, Motivation, and Fidelity in AI Narrative Learning

Investigating how these self-contained films perform across varied instructional modalities could yield valuable insights into their scalability and applicability within diverse educational contexts. Finally, I concur with the author’s observation that medical students are increasingly turning to digital platforms such as social media for information and engagement. Medical educators should take note and examine the factors that make these platforms so compelling.

Tyler Bland

JMIR Med Educ 2025;11:e72336


Enhancing Medical Student Engagement Through Cinematic Clinical Narratives: Multimodal Generative AI–Based Mixed Methods Study

Enhancing Medical Student Engagement Through Cinematic Clinical Narratives: Multimodal Generative AI–Based Mixed Methods Study

This promising pilot study shows potential for scalability and broad applicability of gen AI-enhanced CCNs. The strategy offers a model for transforming how complex medical topics are taught, providing a scalable, engaging solution that can be adapted across different medical content areas to meet evolving educational needs. Our project has limitations in terms of cultural adaptability due to its reliance on specific cultural references and celebrity figures, which may not resonate with all audiences.

Tyler Bland

JMIR Med Educ 2025;11:e63865


Comparing the Accuracy of Two Generated Large Language Models in Identifying Health-Related Rumors or Misconceptions and the Applicability in Health Science Popularization: Proof-of-Concept Study

Comparing the Accuracy of Two Generated Large Language Models in Identifying Health-Related Rumors or Misconceptions and the Applicability in Health Science Popularization: Proof-of-Concept Study

The aims were to explore the accuracy of identifying health-related rumors or misconceptions by GPT-4 and ERNIE Bot 4.0 and to further evaluate the applicability of health science popularization essays generated by them. This study aimed to compare the accuracy of rumor identification and the effectiveness of health science popularization between GPT-4 and ERNIE Bot 4.0, which did not involve the recruitment of human participants.

Yuan Luo, Yiqun Miao, Yuhan Zhao, Jiawei Li, Yuling Chen, Yuexue Yue, Ying Wu

JMIR Form Res 2024;8:e63188