e.g. mhealth
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Skip search results from other journals and go to results- 103 Journal of Medical Internet Research
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Trauma-related diagnostic overshadowing for these likelihood ratings was defined as lower ratings for the target diagnosis (ie, OCD or SUD) or higher ratings for a PTSD diagnosis when trauma exposure was present versus absent. Respondents were then asked to select the primary diagnosis they would assign from the list of diagnoses.
JMIR Ment Health 2025;12:e80801
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Among these devices is “DIANNA” (diagnosis and anamnesis), which has undergone substantial improvements since its initial development. A previous randomized controlled trial provided early insights into the tool’s impact on diagnostic accuracy and efficiency. Building on this foundation, DIANNA now includes a body pictogram feature to better select symptomatic areas, making it more intuitive and comprehensive for clinicians [4,5].
JMIR Form Res 2025;9:e56384
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Large Language Models in Lung Cancer: Systematic Review
In recent years, integrated full-cycle management—covering prevention, screening, diagnosis, treatment, and supportive care—has been promoted to improve both survival and quality of life [7,8]. However, this approach requires complex workflows and large-scale data processing, placing heavy demands on medical resources and personnel.
Artificial intelligence, particularly large language models (LLMs), offers a potential solution.
J Med Internet Res 2025;27:e74177
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Diagnostic and Screening AI Tools in Brazil’s Resource-Limited Settings: Systematic Review
RQ1: Is the tool used for diagnosis or screening?
This question helps clarify the primary objective of each intervention. This distinction is essential, as screening focuses on maximizing sensitivity to ensure few actual cases are missed, while diagnosis aims to confirm or rule out a condition in individuals already identified as at risk, requiring a balance between sensitivity and specificity.
RQ2: What is the context and location of the tool’s application?
JMIR AI 2025;4:e69547
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How to Improve Pancreatic Cancer Network Care Using a Human-Centered Design Sprint
Regarding quality parameters of oncological care, multicenter care in Dutch pancreatic cancer patients is associated with repeated diagnostic investigations, delayed time-to-diagnosis, and delayed time-to-treatment [15]. These studies illustrate the complexity of pancreatic cancer network care and underline the necessity of close collaboration between the non-expert and expert centers.
J Med Internet Res 2025;27:e55598
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It would be advisable to include a comparative analysis to evaluate diagnosis accuracy and the prioritization of additional tests between physicians and LLMs.
Furthermore, the absence of actual data around patient history and other diagnostic parameters beyond what was reported in billing reports (reported as “ground truth” in the study) is a weakness. This can lead to an incomplete or partial diagnosis being labeled as the final diagnosis, leading to miscalculations about the accuracy of LLMs.
JMIRx Med 2025;6:e81235
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Given the recent progress in artificial intelligence (AI), large language models (LLMs) have been proposed to help with various aspects of clinical work, including diagnosis [7]. GPT-4, an LLM developed by Open AI, has shown promise in medical applications with its ability to pass medical board exams in multiple countries and languages [8-11].
JMIRx Med 2025;6:e67661
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The PPI-developed items were organized into six thematic sections: (1) sociodemographic characteristics relevant for quota-based sampling (4 items; eg, sex, age, and federal state); (2) sexual health (2 items; eg, awareness for PSF and awareness for help); (3) self-reported received diagnosis (1 item; presence of CHC and sexual dysfunction diagnosis); (4) biopsychosocial protective and risk factors (2 items; eg, life events, general health status, and interpersonal experience); (5) help-seeking behavior (6 items
J Particip Med 2025;17:e71301
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Delayed diagnosis of VTE is common due to its nonspecific symptoms [29]. VTE can also be difficult to identify in the electronic health record (EHR) due to variability in how VTE is documented and coded [30]. Due to these challenges and the lack of national surveillance, the incidence of VTE is likely underestimated [31,32].
JMIR Med Inform 2025;13:e63720
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Using a Bayesian calibration method based on the Metropolis-Hastings algorithm, we used publicly available diagnosis data for these late-stage complications, in combination with diagnosis data for CHB itself, to back-calculate the prevalence and undiagnosed fraction of CHB.
We developed a state transition model to describe the infection, progression, and treatment process, following the natural history of CHB. A schematic of this model is presented in Figure 1.
JMIR Public Health Surveill 2025;11:e66309
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