e.g. mhealth
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Skip search results from other journals and go to results- 198 Journal of Medical Internet Research
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Therefore, this will produce an open access, contextualized Q&A pair dataset on sexual health in English, which can be used to train AI-enabled health information tools.
JMIR Res Protoc 2025;14:e70005
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Predicting In-Hospital Cardiac Arrest Using Machine Learning Models: Protocol for a Scoping Review
ai
JMIR Res Protoc 2025;14:e69716
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Artificial intelligence (AI) is becoming increasingly popular in the scientific field, as it can provide a fast and easy way to analyze large volumes of data and review records, summarize results, and assist in writing academic papers.
Generative AI refers to a subset of AI technologies that can generate new content, such as text, images, music, speech, video, or code, based on learning to predict the next word or sequence of words given the preceding context [4].
JMIR AI 2025;4:e68592
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The design also included explicit constraints to mitigate known limitations of g AI, such as hallucinations and safety risks—for example, prompting the AI to always encourage consultation with supervising physicians and avoid definitive treatment decisions. Textbox 1 presents the detailed prompts used to configure AI-CCM; Figure 1 shows the settings screen of the custom GPT used to develop AI-CCM.
JMIR Med Educ 2025;11:e76702
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Sentences were segmented from the discharge summaries using the pretrained French spa Cy model (Explosion AI) [19], chosen for its efficiency, robustness, and widespread adoption in NLP pipelines [26]. Given that sentence segmentation is a standard preprocessing step with minimal differences among comparable models [27], no additional comparative analyses were performed.
JMIR Med Inform 2025;13:e67837
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Artificial intelligence (AI)–powered symptom checkers (SCs) have the potential to aid the detection of rare diseases, thereby reducing the time to diagnosis [2,6,9]. AI approaches, as are SCs, are increasingly implemented in health care settings to help alleviate the burden on the systems and to improve the quality of care [10]. The goal of SCs is to provide information to the users that enables them to identify the likely cause of their symptoms [11,12].
JMIR AI 2025;4:e55001
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Free-text clinical notes in EHRs hold valuable insights for population-level quality improvement, but efficient strategies leveraging AI, machine learning, and natural language processing (NLP) are essential to harness this potential.
NLP is useful for analyzing unstructured EHR data in areas like radiology [38], oncology [39,40], endocrinology [41], substance misuse [42], PE identification [43], and postoperative VTE [44].
JMIR Med Inform 2025;13:e63720
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Recognizing the potential of AI, policy makers are promoting its responsible development and integration. A European Parliament study [9] highlights the need for multistakeholder collaboration among AI developers, clinical end users, and biomedical and ethical researchers. Such collaboration is critical for the adoption of AI, as outlined in the European AI Act, the world’s first comprehensive AI regulation, enacted in 2024 [10].
J Med Internet Res 2025;27:e74187
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