IMPORTANCE: Machine learning (ML) algorithms can identify patients with cancer at risk of short-term mortality to inform treatment and advance care planning. However, no ML mortality risk prediction algorithm has been prospectively validated in oncol...
PURPOSE OF REVIEW: The increasing use of robotics in urologic surgery facilitates collection of 'big data'. Machine learning enables computers to infer patterns from large datasets. This review aims to highlight recent findings and applications of ma...
To evaluate the feasibility and safety of the single-port robotic platform by performing nephrectomy in live porcine model. Robotic nephrectomy was performed on sample group of five gilts using the single-port robotic system. The continuous vital...
OBJECTIVES: Early detection of sepsis is critical in clinical practice since each hour of delayed treatment has been associated with an increase in mortality due to irreversible organ damage. This study aimed to develop an explainable artificial inte...
PURPOSE OF REVIEW: Over the last decade, major advancements in artificial intelligence technology have emerged and revolutionized the extent to which physicians are able to personalize treatment modalities and care for their patients. Artificial inte...
PURPOSE OF REVIEW: Pathomics, the fusion of digitalized pathology and artificial intelligence, is currently changing the landscape of medical pathology and biologic disease classification. In this review, we give an overview of Pathomics and summariz...
BACKGROUND: Accurate diagnostic strategies to identify SARS-CoV-2 positive individuals rapidly for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PC...
OBJECTIVES: Bacteremia and fungemia can cause life-threatening illness with high mortality rates, which increase with delays in antimicrobial therapy. The objective of this study is to develop machine learning models to predict blood culture results ...
OBJECTIVE: This study aimed to use the radiomics signatures of a machine learning-based tool to evaluate the prognosis of patients with coronavirus disease 2019 (COVID-19) infection.
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