e.g. mhealth
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Skip search results from other journals and go to results- 14 Journal of Medical Internet Research
- 9 JMIR Medical Informatics
- 8 JMIR AI
- 6 JMIR Formative Research
- 6 JMIR Human Factors
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Integrating Nurse Preferences Into AI-Based Scheduling Systems: Qualitative Study
Furthermore, machine learning (ML) techniques, such as random forests, can be used to predict scheduling preferences based on historical data and patterns [32].
Currently, to our knowledge, there is no comprehensive framework that integrates nurses’ requirements for participatory shift scheduling within a broader set of methodologies. This paper aims to address this gap and present preliminary findings to facilitate broader implementation.
JMIR Form Res 2025;9:e67747
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AI, broadly defined as the ability of computer systems to mimic human cognitive functions, encompasses various computational subfields, including machine learning (ML). Furthermore, deep learning (DL), a subset of ML, uses algorithms modeled after human neurons to detect complex patterns and relationships in data [6]. These AI technologies have shown promising applications in dermatology, ranging from identifying skin malignancies to classifying inflammatory skin conditions and analyzing clinical notes.
JMIR Dermatol 2025;8:e67154
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Framing the Human-Centered Artificial Intelligence Concepts and Methods: Scoping Review
A total of 4 investigators (RB, EM, TB, and ML) working in pairs performed screening, selection, and data extraction. Specifically, 2 blinded reviewers assessed the title and abstracts to define eligibility for full-text assessment, and, at the subsequent step, assessed full-texts for inclusion. Disagreement was resolved by consensus.
JMIR Hum Factors 2025;12:e67350
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Accordingly, we formulated an ML-based framework to identify participants using the clinical laboratory test values of candidates. In this study, we chose to compare the ML-based method with a random selection method, which we considered representative of the common practice in clinical settings where patient lists are screened sequentially.
JMIR AI 2025;4:e64845
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Association Between Risk Factors and Major Cancers: Explainable Machine Learning Approach
In conclusion, our study established a predictive framework using EHR data to assess the association between risk factors and cancer outcomes using explainable ML models across major cancer types. We reported critical nontraditional chronic condition risk factors in addition to common demographic risk factors and outlined distinct patterns for each of the 4 cancer types studied. Additionally, we explored the similarities and differences in risk factor patterns across these cancers.
JMIR Cancer 2025;11:e62833
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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.
LLMs are used in generating related search terms, expanding upon human-generated queries to enhance the comprehensiveness of literature searches. Any LLM can be adapted to TU software, up to date we have used Chat GPT 4 [18].
JMIR Res Protoc 2025;14:e67248
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