BACKGROUND: Machine learning algorithms may contribute to improving maternal and child health, including determining the suitability of caesarean section (CS) births in low-resource countries. Despite machine learning algorithms offering a more robus...
BACKGROUND AND OBJECTIVES: Distinguishing neurodegenerative diseases is a challenging task requiring neurologic expertise. Clinical decision support systems (CDSSs) powered by machine learning (ML) and artificial intelligence can assist with complex ...
The European journal of general practice
Jun 6, 2025
BACKGROUND: Generative AI (Gen AI) is frequently cited as an innovation to address the current challenges in healthcare, also for primary care. Examples include automating tasks like voice-to-notes transcription or chatbots using large language model...
BACKGROUND: Potentially inappropriate prescribing in outpatient care contributes to adverse outcomes and health care inefficiencies. Clinical decision support systems (CDSS) offer promising solutions, but their effectiveness is often constrained by i...
BMC medical informatics and decision making
May 16, 2025
BACKGROUND: This study aims to understand how secondary use of health records can be done for prediction, detection, treatment recommendations, and related tasks in clinical decision support systems.
PURPOSE: Low-dose computed tomography (LDCT) screening is effective in reducing lung cancer mortality by detecting the disease at earlier, more treatable stages. However, high false-positive rates and the associated risks of subsequent invasive diagn...
BACKGROUND: Clinical decision support systems (CDSSs) play a pivotal role in health care by enhancing clinical decision-making processes. These systems represent a significant advancement in medical information systems. However, optimizing their effe...
IEEE transactions on neural networks and learning systems
May 2, 2025
Dynamic treatment regimes (DTRs), which comprise a series of decisions taken to select adequate treatments, have attracted considerable attention in the clinical domain, especially from sepsis researchers. Existing sepsis DTR learning studies are mai...
IMPORTANCE: Large language models (LLMs) have not yet been compared with traditional diagnostic decision support systems (DDSSs) on unpublished clinical cases.
BACKGROUND: AI-driven prediction algorithms have the potential to enhance emergency medicine by enabling rapid and accurate decision-making regarding patient status and potential deterioration. However, the integration of multimodal data, including r...
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