AIMC Topic: Clinical Decision-Making
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Generative artificial intelligence in oncology.
Current opinion in urology
Feb 28, 2025
PURPOSE OF REVIEW: By leveraging models such as large language models (LLMs) and generative computer vision tools, generative artificial intelligence (GAI) is reshaping cancer research and oncologic practice from diagnosis to treatment to follow-up. ...
Deep Learning for Lumbar Disc Herniation Diagnosis and Treatment Decision-Making Using Magnetic Resonance Imagings: A Retrospective Study.
World neurosurgery
Feb 26, 2025
BACKGROUND: Lumbar disc herniation (LDH) is a common cause of back and leg pain. Diagnosis relies on clinical history, physical exam, and imaging, with magnetic resonance imaging (MRI) being an important reference standard. While artificial intellige...
Machine learning for temporary stoma after intestinal resection in surgical decision-making of Crohn's disease.
BMC gastroenterology
Feb 25, 2025
BACKGROUND: Crohn's disease (CD) often necessitates surgical intervention, with temporary stoma creation after intestinal resection (IR) being a crucial decision. This study aimed to construct novel models based on machine learning (ML) to predict te...
Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review.
BMC medical research methodology
Feb 21, 2025
BACKGROUND: This scoping review systematically maps externally validated machine learning (ML)-based models in cancer patient care, quantifying their performance, and clinical utility, and examining relationships between models, cancer types, and cli...
Artificial intelligence in neurovascular decision-making: a comparative analysis of ChatGPT-4 and multidisciplinary expert recommendations for unruptured intracranial aneurysms.
Neurosurgical review
Feb 21, 2025
In the multidisciplinary treatment of cerebrovascular diseases, specialists from different disciplines strive to develop patient-specific treatment recommendations. ChatGPT is a natural language processing chatbot with increasing applicability in med...
Fast Interpretable Greedy-Tree Sums.
Proceedings of the National Academy of Sciences of the United States of America
Feb 14, 2025
Modern machine learning has achieved impressive prediction performance, but often sacrifices interpretability, a critical consideration in high-stakes domains such as medicine. In such settings, practitioners often use highly interpretable decision t...
Predictive modeling of methadone poisoning outcomes in children ≤ 5 years: utilizing machine learning and the National Poison Data System for improved clinical decision-making.
European journal of pediatrics
Feb 11, 2025
UNLABELLED: The escalating therapeutic use of methadone has coincided with an increase in accidental ingestions, particularly among children ≤ 5 years. This study utilized machine learning (ML) methodologies on data from the National Poison Data Syst...
Prevention and management of degenerative lumbar spine disorders through artificial intelligence-based decision support systems: a systematic review.
BMC musculoskeletal disorders
Feb 7, 2025
BACKGROUND: Low back pain is the leading cause of disability worldwide with a significant socioeconomic burden; artificial intelligence (AI) has proved to have a great potential in supporting clinical decisions at each stage of the healthcare process...
German surgeons' perspective on the application of artificial intelligence in clinical decision-making.
International journal of computer assisted radiology and surgery
Feb 5, 2025
PURPOSE: Artificial intelligence (AI) is transforming clinical decision-making (CDM). This application of AI should be a conscious choice to avoid technological determinism. The surgeons' perspective is needed to guide further implementation.
Integrating machine learning for treatment decisions in anterior open bite orthodontic cases: A retrospective study.
Journal of the World federation of orthodontists
Jan 31, 2025
INTRODUCTION: This article explores the integration of machine learning (ML) algorithms to aid in treatment planning and extraction decisions for anterior open bite cases, leveraging demographic, clinical, and radiographic data to predict treatment o...
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