AIMC Topic: Artificial Intelligence

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[Potential for improvement by new resection and imaging techniques in TUR-B].

Aktuelle Urologie
Transurethral resection of bladder tumors (TURB) is the cornerstone in urological care of bladder cancer patients. Since the introduction of resectoscopes almost 100 years ago, little has changed in the basic resection technique. The further dissemin...

Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review.

BioMed research international
BACKGROUND: The purpose of this review is to depict current research and impact of artificial intelligence/machine learning (AI/ML) algorithms on dialysis and kidney transplantation. Published studies were presented from two points of view: What medi...

Artificial intelligence for breast cancer detection in mammography and digital breast tomosynthesis: State of the art.

Seminars in cancer biology
Screening for breast cancer with mammography has been introduced in various countries over the last 30 years, initially using analog screen-film-based systems and, over the last 20 years, transitioning to the use of fully digital systems. With the in...

Artificial Intelligence Applications in Otology: A State of the Art Review.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: Recent advances in artificial intelligence (AI) are driving innovative new health care solutions. We aim to review the state of the art of AI in otology and provide a discussion of work underway, current limitations, and future directions.

Hematologist-Level Classification of Mature B-Cell Neoplasm Using Deep Learning on Multiparameter Flow Cytometry Data.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
The wealth of information captured by multiparameter flow cytometry (MFC) can be analyzed by recent methods of computer vision when represented as a single image file. We therefore transformed MFC raw data into a multicolor 2D image by a self-organiz...

The four dimensions of contestable AI diagnostics - A patient-centric approach to explainable AI.

Artificial intelligence in medicine
The problem of the explainability of AI decision-making has attracted considerable attention in recent years. In considering AI diagnostics we suggest that explainability should be explicated as 'effective contestability'. Taking a patient-centric ap...

Weakly-supervised learning for lung carcinoma classification using deep learning.

Scientific reports
Lung cancer is one of the major causes of cancer-related deaths in many countries around the world, and its histopathological diagnosis is crucial for deciding on optimum treatment strategies. Recently, Artificial Intelligence (AI) deep learning mode...

Operational framework and training standard requirements for AI-empowered robotic surgery.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: For autonomous robot-delivered surgeries to ever become a feasible option, we recommend the combination of human-centered artificial intelligence (AI) and transparent machine learning (ML), with integrated Gross anatomy models. This can b...

Utilization of a Deep Learning Algorithm for Microscope-Based Fatty Vacuole Quantification in a Fatty Liver Model in Mice.

Toxicologic pathology
Quantification of fatty vacuoles in the liver, with differentiation from lumina of liver blood vessels and bile ducts, is an example where the traditional semiquantitative pathology assessment can be enhanced with artificial intelligence (AI) algorit...