e.g. mhealth
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Skip search results from other journals and go to results- 41 Journal of Medical Internet Research
- 19 JMIR Formative Research
- 12 JMIR mHealth and uHealth
- 11 JMIR Medical Informatics
- 7 JMIR Public Health and Surveillance
- 7 JMIR Research Protocols
- 6 JMIR Cardio
- 6 JMIR Medical Education
- 4 Interactive Journal of Medical Research
- 4 JMIR Mental Health
- 3 JMIR AI
- 3 JMIR Biomedical Engineering
- 3 JMIR Human Factors
- 3 JMIR Infodemiology
- 3 JMIR Pediatrics and Parenting
- 2 JMIR Perioperative Medicine
- 1 Iproceedings
- 1 JMIR Aging
- 1 JMIR Cancer
- 1 JMIR Dermatology
- 1 JMIR Rehabilitation and Assistive Technologies
- 0 Medicine 2.0
- 0 iProceedings
- 0 JMIR Serious Games
- 0 JMIR Preprints
- 0 JMIR Bioinformatics and Biotechnology
- 0 JMIR Challenges
- 0 JMIR Diabetes
- 0 JMIR Data
- 0 Journal of Participatory Medicine
- 0 JMIR Nursing
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- 0 Asian/Pacific Island Nursing Journal
- 0 Online Journal of Public Health Informatics
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Moreover, mean absolute error, mean absolute percentage error (MAPE), 5% accuracy (percentage of MAPE within a 5% range of the reference value), root-mean-squared error (RMSE), and ordinary least squares linear regression were used to evaluate accuracy.
JMIR Cardio 2025;9:e67110
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Notably, when trained on dataset #1, the SBERT-Doc SCAN algorithm emerges as the leading performer, securing an accuracy and F1-score of 0.8985 on the test dataset. This standout performance highlights the algorithm’s capability to classify medical domain data with high precision. Additionally, the SBERT-MEC algorithm also displays comparable performance on the same dataset, with an accuracy and F1-score of 0.8938, making it the second most effective algorithm in our evaluation.
JMIR Form Res 2025;9:e54803
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The accuracy of an SRD is crucial in its creation. Furthermore, traceability and accountability are essential considerations. The use of LLMs like Chat GPT often results in the original authors and sources not being cited, leading to the misattribution of information [13].
This study has 2 aims. The first is to quantify the challenges of SRD creation by gathering the opinions of medical information professionals regarding the time consumption of the various steps of SRD development.
JMIR AI 2025;4:e55277
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GPT-3.5 Turbo and GPT-4 Turbo in Title and Abstract Screening for Systematic Reviews
This study aimed to compare accuracy and efficiency between GPT-3.5 Turbo and GPT-4 Turbo (Open AI)—widely used LLMs in the medical field—in title and abstract screening.
We conducted a post hoc analysis of our previous study to evaluate the performance of GPT-3.5 Turbo and GPT-4 Turbo in LLM-assisted title and abstract screening, using data from 5 clinical questions (CQs) developed for the Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock 2024 [6,10].
JMIR Med Inform 2025;13:e64682
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Accuracy of Large Language Models for Literature Screening in Thoracic Surgery: Diagnostic Study
However, their accuracy in the screening process for meta-analyses remains unclear.
We hypothesized that LLM-assisted literature screening could achieve accuracy comparable with manual screening. To test this hypothesis, we designed a diagnostic trial using conventional manual screening as the reference standard to evaluate the accuracy of LLM-assisted literature screening.
This prospective diagnostic study aimed to assess the validity of LLMs for assisting with literature screening during meta-analysis.
J Med Internet Res 2025;27:e67488
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Evidence on Digital HIV Self-Testing From Accuracy to Impact: Updated Systematic Review
A systematic review comparing the accuracy of HIV self-tests conducted by the general population to that of diagnostic tests performed by health care practitioners concluded that HIV self-testing innovations were a reliable and accurate method of testing when used by the general population [8]. Although these reviews provide valuable insights into the accuracy of HIV self-testing, no systematic reviews have yet assessed the impact of digital innovations on HIV self-testing accuracy.
J Med Internet Res 2025;27:e63110
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The app quantified the three following finger-tapping characteristics: (1) speed of finger-tapping for 10 seconds against literature-based standards, (2) comparison of finger-tapping speed on the left versus right hand, and (3) the accuracy of tapping in the middle of the target provided. Participants’ responses to questions and their finger-tapping statistics were aggregated into an ECHAS score.
JMIR Form Res 2025;9:e60465
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We then calculated sensitivity, specificity, and accuracy for all 3 syndrome definitions, along with 95% CI by using each ED case’s hospital discharge diagnosis as a reference standard. We defined the gold standard for the validation of the ED syndromes based on the following criteria: for ACS, cases with the ICD-10 hospital discharge diagnoses I20 or I21; for MI, cases with discharge diagnosis of I21; and for STR, discharge diagnoses of I60, I61, I63, or I64 were considered as true positives.
JMIR Public Health Surveill 2025;11:e66218
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Final analyses included examination of classification accuracy, using forward entry stepwise discriminant function analyses to examine the ability of the 6 LASSI variables in each version to classify participants as a MCI versus NC. We then added ADAS-Cog scores to the classification model to see if classification was improved by a standard cognitive assessment targeting AD/ADRD.
JMIR Ment Health 2025;12:e64716
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The hypothesis was that the software would demonstrate comparable accuracy and validity in heart rate measurement during a 6-minute walking test (6 MWT) and a 47-minute interval training session, matching the performance of a certified ECG, which is considered the gold standard.
The prospective interventional PROTEGO MAXIMA trial was designed to evaluate the validity, accuracy, and safety of a prehabilitation app.
JMIR Mhealth Uhealth 2025;13:e55298
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