e.g. mhealth
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Skip search results from other journals and go to results- 62 Journal of Medical Internet Research
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In the last decades, depression screening has been increasingly discussed as promising to reach those affected but undetected at an early stage. In addition to population-level screening in routine clinical care, as, for example, recommended in the United States [3], advocates also speak out in favor of screening for depression on the web [4]. For many affected individuals, the web is already the favored source for information on mental health [5,6].
J Med Internet Res 2025;27:e59476
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The literature screening was performed using the Covidence systematic review software. After removing duplicate articles, the title and the abstract screen were performed by 1 reviewer per article (SI, OE, and AD). The studies selected for full-text review were screened by 2 independent assessors (SI, OE, and AD) against the eligibility criteria, with conflicts managed with discussion and with the assistance of an expert reviewer (BB).
J Med Internet Res 2025;27:e57019
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Data collected using the reference devices will be stored without any identifiable participant information and will only be identifiable using the participant identification number provided during screening.
The raw data of participants’ vital signs will be locked, encrypted, and accessible only to the study center team. Raw data can be processed only after unlocking the files to calculate the final measured results.
JMIR Res Protoc 2025;14:e65229
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Telemedicine Booths for Screening Cardiovascular Risk Factors: Prospective Multicenter Study
Various screening programs have been set up over the years to target people who rarely consult a GP. These programs have often focused on detecting hypertension, also known as the “silent killer,” and they have been run in a variety of different settings [15-17]. Some screening programs have involved health checks carried out by nurses, pharmacists, or medical students in settings such as pharmacies, dental surgeries, and community centers [18].
JMIR Hum Factors 2025;12:e57032
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As a result, a basic assessment for delirium is recommended for all hospitalized patients aged 65 years or older [5], and formal screening for delirium is recommended for critically ill patients [6].
Despite these recommendations, delirium frequently remains undiagnosed [7]. An automated delirium prediction tool could help address this, by alerting clinicians to at-risk patients so that they could be more carefully assessed for delirium.
JMIR Med Inform 2025;13:e60442
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To identify eligible patients and to gather information on the prevalence of the target group in primary care, a screening survey will be conducted (see Data Collection and Outcomes section below).
JMIR Res Protoc 2025;14:e66335
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Significant scientific evidence shows that early screening and timely treatment referral can prevent most visual loss caused by DR [11]. Conventionally, DR screening (DRS) includes fundus (retina) examination by ophthalmologists or color fundus photography using conventional cameras (mydriatic or nonmydriatic) conducted by trained eye technicians or optometrists [12].
JMIR Form Res 2025;9:e67047
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The AI Reviewer: Evaluating AI’s Role in Citation Screening for Streamlined Systematic Reviews
However, the conventional systematic review methodology is time-consuming, particularly the manual screening of articles for pertinence [2]. The exponential increase in biomedical literature presents a challenge for researchers to remain updated. Artificial intelligence (AI) has shown promise in various fields [3], with large language models (LLMs) specifically offering capabilities to interpret complex text, which can be leveraged in the systematic review process [4].
JMIR Form Res 2025;9:e58366
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Deidentified data were extracted from electronic health records (EHRs) of the national cervical cancer screening program in China. In summary, our study included eligible women aged 25‐65 years who participated in the cervical cancer screening. Data from Fujian Province (2014‐2023) were used to establish a discovery cohort to train the models.
JMIR Public Health Surveill 2025;11:e67840
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GPT-3.5 Turbo and GPT-4 Turbo in Title and Abstract Screening for Systematic Reviews
Manual citation screening, however, is a time-consuming and labor-intensive process, often resulting in human errors and increased workloads [1,2]. Large language models (LLMs) have demonstrated the ability to comprehend and process natural language, underscoring their utility in medical applications [3]. Consequently, LLMs have emerged as promising tools for citation screening in systematic reviews [4].
JMIR Med Inform 2025;13:e64682
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