AIMC Topic: Decision Support Systems, Clinical

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A deep learning-based clinical decision support system for glioma grading using ensemble learning and knowledge distillation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Gliomas are the most common malignant primary brain tumors, and grading their severity, particularly the diagnosis of low-grade gliomas, remains a challenging task for clinicians and radiologists. With advancements in deep learning and medical image ...

Multi-task reinforcement learning and explainable AI-Driven platform for personalized planning and clinical decision support in orthodontic-orthognathic treatment.

Scientific reports
This study presents a novel clinical decision support platform for orthodontic-orthognathic treatment that integrates multi-task reinforcement learning with explainable artificial intelligence. The platform addresses the challenges of personalized tr...

Improving AI-Based Clinical Decision Support Systems and Their Integration Into Care From the Perspective of Experts: Interview Study Among Different Stakeholders.

JMIR medical informatics
BACKGROUND: Artificial intelligence (AI)-based systems are receiving increasing attention in the health care sector. While the use of AI is well advanced in some medical applications, such as image recognition, it is still in its infancy in others, s...

Prediction of caesarean section birth using machine learning algorithms among pregnant women in a district hospital in Ghana.

BMC pregnancy and childbirth
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...

Leveraging multithreading on edge computing for smart healthcare based on intelligent multimodal classification approach.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Medical digitization has been intensively developed in the last decade, leading to paving the path for computer-aided medical diagnosis research. Thus, anomaly detection based on machine and deep learning techniques has been extensively employed in h...

PED-IA, a CDSS to support decision in pediatrics telephone triage: a crossover evaluation.

Computers in biology and medicine
BACKGROUND: Pediatric emergency departments face overcrowding, often driven by non-urgent consultations. Telephone triage, supported by clinical decision support systems (CDSSs), offers a potential solution to improve decision accuracy and reduce unn...

Toward responsible artificial intelligence in medicine: Reflections from the Australian epilepsy project.

Artificial intelligence in medicine
Artificial intelligence (AI) is a multidisciplinary scientific field that uses machines to solve real-world problems and predict outcomes. Despite the current enthusiasm about AI's potential as a clinical support tool, there is also a growing awarene...

Evaluation of Diagnostic Recommendations Embedded in Medication Alerts: Prospective Single-Arm Interventional Study.

Journal of medical Internet research
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...

Secondary use of health records for prediction, detection, and treatment planning in the clinical decision support system: a systematic review.

BMC medical informatics and decision making
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.

Optimizing Strategy for Lung Cancer Screening: From Risk Prediction to Clinical Decision Support.

JCO clinical cancer informatics
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...