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Skip search results from other journals and go to results- 99 Journal of Medical Internet Research
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The study results were reported in accordance with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis plus Artificial Intelligence (TRIPOD+AI) statement [45].
Baseline characteristics of participants in this study are shown in Table 1. The participants in the internal validation dataset had a mean age of 71.0 years. Most of the participants were male and had finished primary school.
JMIR Aging 2025;8:e62942
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Reference 2: The comparative sufficiency of ChatGPT, Google Bard, and Bing AI in answering diagnosis,diagnosis
JMIR Dermatol 2025;8:e72540
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Reference 1: The comparative sufficiency of ChatGPT, Google Bard, and Bing AI in answering diagnosis,diagnosis
JMIR Dermatol 2025;8:e71768
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AI-Derived Blood Biomarkers for Ovarian Cancer Diagnosis: Systematic Review and Meta-Analysis
The conventional diagnosis of OC principally depends on imaging techniques (encompassing ultrasound, computed tomography, and magnetic resonance imaging); serum biomarkers (such as cancer antigen 125, carcinoembryonic antigen, and human epididymis protein 4); along with the invasive procedure (histological biopsy) [3,4]. However, the sensitivity and specificity of imaging techniques and biomarkers are restricted [5]. Furthermore, the histopathological test is inherently invasive [3].
J Med Internet Res 2025;27:e67922
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These projects include the following:
Chameleon: a project focused on developing AI algorithms for cancer diagnosis and prognosis.
Eu Can Image: a project aimed at creating a large-scale cancer image database.
Pro CAncer-I: a project focused on developing AI-based tools for personalized cancer treatment.
Incisive: a project contributing a significant amount of cancer image data to EUCAIM.
Primage: a project focused on developing AI-based image analysis techniques for cancer diagnosis.
J Med Internet Res 2025;27:e66273
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GPT-4 Omni and GPT-4 Turbo demonstrate low accuracy and low specificity for melanoma diagnosis.
Accuracy of Chat GPT-4o in diagnosing melanoma and “not melanoma” with binary versus nonbinary prompting.
Currently, GPT engines demonstrate low accuracy for diagnosing melanoma. Higher diagnostic accuracies have been achieved using neural networks such as Moleanalyzer pro (87.7%) and Chat GPT Vision (85%); however, these studies included much smaller sample sizes of 100 and 60 images, respectively [7,8].
JMIR Dermatol 2025;8:e67551
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In response to the pandemic, an e Consult service operating in Ontario, Canada, launched a long COVID specialist group, allowing PCPs to connect with specialists across multiple disciplines with expertise in long COVID to help with the diagnosis and management of their patients. Previous studies assessing this e Consult service in Ontario have demonstrated improved access to specialty care, reduced need for face-to-face specialist visits, cost savings, and high physician satisfaction [4-8].
JMIR Hum Factors 2025;12:e58582
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Stroke Diagnosis and Prediction Tool Using ChatGLM: Development and Validation Study
The objective of this study was to develop an acute stroke diagnosis tool that guides key therapies based on Chat GLM-6 B and to verify its accuracy in diagnosing and predicting strokes. The application of this model in primary care could enhance the standard workflow for stroke diagnosis, identify patients who could benefit from recanalization, and facilitate risk prediction.
J Med Internet Res 2025;27:e67010
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We aimed to identify the clinical features that are associated with earlier diagnosis of dementia from the first memory loss complaints. Results indicated that the location of the first complaints made and the diagnosis, as well as the relation of the primary caregiver, were significantly associated with earlier diagnosis of dementia.
JMIR Aging 2025;8:e65221
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It included ICD-10-GM discharge diagnoses (further referred to as “hospital discharge diagnoses”), as well as information on diagnosis certainty and whether they were labeled as the “main diagnosis.” The information was routinely collected during inpatient treatment according to the German social laws regulating the billing of inpatient services and obliging hospitals to transmit billing data to a federal agency (§ 21 Krankenhausentgeltgesetz, KHEntg G).
JMIR Public Health Surveill 2025;11:e66218
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