e.g. mhealth
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Skip search results from other journals and go to results- 24 Journal of Medical Internet Research
- 17 JMIR Formative Research
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This study aimed to assess the performance of NLP algorithms compared to conventional methods for detecting fall incidence and the mechanism of falls obtained from clinical notes of patients with hip fractures. We hypothesize that NLP algorithms outperform fall ICD codes in detecting falls and their mechanisms in patients with hip fractures.
A retrospective case-control study was conducted, including the data from 4 tertiary hospitals in Greater Boston, Massachusetts.
JMIR Med Inform 2025;13:e66973
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A more detailed technical description of the algorithms and processes is available in Multimedia Appendix 1.
As AI evolves, fully automated synthesis workflows may become feasible, but rigorous validation is essential to ensure trust, transparency, and scientific integrity.
The implementation of new methodologies in the scientific field requires a comparative validation process with established methods to confirm their reliability and effectiveness.
JMIR Form Res 2025;9:e75215
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It is however, for many diseases still unclear whether telemonitoring does lead to more efficient care delivery, as it requires a significant time investment (eg, alert processing, development and implementation of telemonitoring algorithms), generates new data, and needs continuous optimization of clinical workflows [6,7].
Modern telemonitoring platforms are often embedded in existing care paths and include clinical algorithms that triage and process the generated alerts [3,8].
JMIR Med Inform 2025;13:e66066
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TU is primarily a software-driven system engineered to streamline tertiary evidence synthesis, relying on programmed algorithms to automate the majority of its functions. The core of the system is built on a software infrastructure that processes and synthesizes data from SR/MA abstracts stored in MEDLINE. While AI plays a crucial role, particularly through the use of LLMs and machine learning (ML), it is used selectively within the broader software framework to enhance specific tasks.
JMIR Res Protoc 2025;14:e67248
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The long-term benefit is the ability to provide a basis for evaluating quality of care and benchmarking key metrics for quality improvement efforts and to leverage machine learning (ML) and artificial intelligence (AI) to enhance future care delivery in the tactical environment, as well as to inform clinical decision support systems and algorithms deployed in these settings [5].
JMIR Res Protoc 2025;14:e67673
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Generative AI Models in Time-Varying Biomedical Data: Scoping Review
We have organized the paper as follows: (1) overview of the algorithms and techniques introduced in the review; (2) structured data; (3) unstructured data; (4) medical imaging; (5) physiological waveforms; (6) genetics and multi-omics data; and (7) ethical considerations, challenges, and future directions.
Health care data sources in different modalities for generative artificial intelligence (AI) application. ECG: electrocardiogram; EEG: electroencephalogram.
J Med Internet Res 2025;27:e59792
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Trends and Gaps in Digital Precision Hypertension Management: Scoping Review
Digital tools used included mobile phones (2/4, 50%), web platform (1/4, 25%), electronic health record (EHR; 1/4, 25%), machine learning (ML) algorithms (1/4, 25%), BP monitor (1/4, 25%), and genomic databases (1/4, 25%).
Summary of the studies on digital phenotyping for HTNa.
a HTN: hypertension.
b BP: blood pressure.
c EHR: electronic health record.
d ML: machine learning.
e SBP: systolic blood pressure.
Of the 4 studies, 2 (50%) used secondary data analysis to apply phenotyping.
J Med Internet Res 2025;27:e59841
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Since i GCTs patients exhibit facial feature changes compared with normal children, we hypothesize that facial recognition algorithms can be developed to alert clinicians at the initial consultation, providing personalized diagnostic approaches [9,10].
The application of facial recognition algorithms is extensive, especially those based on machine learning algorithms, which have been profoundly studied in the medical field.
J Med Internet Res 2025;27:e58760
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Increasing interest in deep learning algorithms has also led to the emergence of new diagnostic strategies based on image processing (see [12,13] for reviews). Although most of them have been developed from cystoscopy images, some aim to propose noninvasive techniques and have exploited images obtained from urine cytology [14-16].
J Med Internet Res 2025;27:e56946
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