BACKGROUND: Artificial intelligence is becoming a part of daily life and the medical field. Generative artificial intelligence models, such as GPT-4 and ChatGPT, are experiencing a surge in popularity due to their enhanced performance and reliability...
BACKGROUND: The increasing use of direct-to-consumer artificial intelligence (AI)-enabled mobile health (AI-mHealth) apps presents an opportunity for more effective health management and monitoring and expanded mobile health (mHealth) capabilities. H...
BACKGROUND: Polycystic ovary syndrome (PCOS) is a common reproductive and metabolic disorder in the reproductive-age women. The international evidence-based guideline for the assessment and management of PCOS 2023 now suggests raising the follicle nu...
OBJECTIVES: To establish and validate deep learning (DL) models based on pre-treatment multiparametric magnetic resonance imaging (MRI) images of primary rectal cancer and basic clinical data for the prediction of synchronous liver metastases (SLM) i...
INTRODUCTION: AI has the potential to enhance diagnostics, optimize treatment planning, and improve patient care. However, concerns remain regarding professional autonomy, ethical considerations, and the need for adequate training. This research aims...
BACKGROUND: This retrospective observational research evaluates the potential applicability of artificial intelligence models to predict the length of hospital stay for patients with pleural empyema who underwent uniportal video-assisted thoracoscopi...
BMC medical informatics and decision making
May 19, 2025
BACKGROUND: Despite the global commitment to ending AIDS by 2030, the loss of follow-up (LTFU) in HIV care remains a significant challenge. To address this issue, a data-driven clinical decision tool is crucial for identifying patients at greater ris...
OBJECTIVES: This study aims to develop and validate a novel radiomics model utilizing magnetic resonance imaging (MRI) to predict progression-free survival (PFS) in patients with unresectable hepatocellular carcinoma (uHCC) who are receiving a combin...
OBJECTIVE: Development of a deep learning model for accurate preoperative identification of glioblastoma and solitary brain metastases by combining multi-centre and multi-sequence magnetic resonance images and comparison of the performance of differe...
Identifying likely placebo responders can help design more efficient clinical trials by stratifying participants, reducing sample size requirements, and enhancing the detection of true drug effects. In response to this need, we developed a deep convo...
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