AIMC Topic: Artificial Intelligence

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The future is bright: artificial intelligence for trainee medical officers in Australia and New Zealand.

Internal medicine journal
Given their frontline role in Australia and Aotearoa New Zealand (ANZ) healthcare, trainee medical officers (TMOs) will play a crucial role in the development and use of artificial intelligence (AI) for clinical care, ongoing medical education and re...

The Promise of Artificial Intelligence in Peyronie's Disease.

Current urology reports
PURPOSE OF REVIEW: The application of artificial intelligence (AI) to enhance clinical decision-making in Peyronie's disease (PD) has generated significant interest. This review explores the current landscape of AI in PD evaluation.

AI and neurosurgery: a new era of enhanced outcomes.

Neurosurgical review
Artificial Intelligence (AI) is revolutionizing neurosurgery by enhancing diagnostic accuracy, surgical planning, and personalized patient care. Despite challenges like data privacy and bias, AI's integration promises to transform surgical outcomes a...

AI-based lumbar central canal stenosis classification on sagittal MR images is comparable to experienced radiologists using axial images.

European radiology
OBJECTIVES: The assessment of lumbar central canal stenosis (LCCS) is crucial for diagnosing and planning treatment for patients with low back pain and neurogenic pain. However, manual assessment methods are time-consuming, variable, and require axia...

AI-Safe-C score: Assessing liver-related event risks in patients without cirrhosis after successful direct-acting antiviral treatment.

Journal of hepatology
BACKGROUND & AIMS: Direct-acting antivirals (DAAs) have considerably improved chronic hepatitis C (HCV) treatment; however, follow-up after sustained virological response (SVR) typically neglects the risk of liver-related events (LREs). This study in...

Online Adaptive Proton Therapy Facilitated by Artificial Intelligence-Based Autosegmentation in Pencil Beam Scanning Proton Therapy.

International journal of radiation oncology, biology, physics
PURPOSE: Online adaptive proton therapy (oAPT) is essential to address interfractional anatomical changes in patients receiving pencil beam scanning proton therapy. Artificial intelligence (AI)-based autosegmentation can increase the efficiency and a...

Quantitative characterization of eosinophilia in nasal polyps with AI-based single cell classification.

International forum of allergy & rhinology
Eosinophilic granulocytes have characteristic morphological features. This makes them prime candidates for utilization of a single cell binary classification network. Single cell binary classification networks can reliably help quantify eosinophils i...