AIMC Topic: Humans

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Clinical Explainability Failure (CEF) & Explainability Failure Ratio (EFR) - Changing the Way We Validate Classification Algorithms.

Journal of medical systems
Adoption of Artificial Intelligence (AI) algorithms into the clinical realm will depend on their inherent trustworthiness, which is built not only by robust validation studies but is also deeply linked to the explainability and interpretability of th...

The association between perineural invasion in mpMRI-targeted and/or systematic prostate biopsy and adverse pathological outcomes in robot-assisted radical prostatectomy.

Actas urologicas espanolas
INTRODUCTION AND OBJECTIVES: This study aims to investigate the relationship between perineural invasion (PNI) in targeted (TBx) and/or systematic (SBx) prostate needle biopsy and adverse pathological features of prostate cancer (PCa) in prostatectom...

Challenges in translational machine learning.

Human genetics
Machine learning (ML) algorithms are increasingly being used to help implement clinical decision support systems. In this new field, we define as "translational machine learning", joint efforts and strong communication between data scientists and cli...

Uncertainty-aware hierarchical segment-channel attention mechanism for reliable and interpretable multichannel signal classification.

Neural networks : the official journal of the International Neural Network Society
Multichannel signal data analysis has been crucial in various industrial applications, such as human activity recognition, vehicle failure predictions, and manufacturing equipment monitoring. Recently, deep neural networks have come into use for mult...

Radiation and iodine dose reduced thoraco-abdomino-pelvic dual-energy CT at 40 keV reconstructed with deep learning image reconstruction.

The British journal of radiology
OBJECTIVE: To evaluate the feasibility of a simultaneous reduction of radiation and iodine doses in dual-energy thoraco-abdomino-pelvic CT reconstructed with deep learning image reconstruction (DLIR).

Deep learning improves image quality and radiomics reproducibility for high-speed four-dimensional computed tomography reconstruction.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Hybrid iterative reconstruction (HIR) is the most commonly used algorithm for four-dimensional computed tomography (4DCT) reconstruction due to its high speed. However, the image quality is worse than that of model-based itera...

Remote telesurgery in humans: a systematic review.

Surgical endoscopy
BACKGROUND: Since the conception of robotic surgery, remote telesurgery has been a dream upon which incredible technological advances haven been built. Despite the considerable enthusiasm for, there have been few published studies of remote telesurge...

Utilizing Conversational Artificial Intelligence, Voice, and Phonocardiography Analytics in Heart Failure Care.

Heart failure clinics
Conversational artificial intelligence involves the ability of computers, voice-enabled devices to interact intelligently with the user through voice. This can be leveraged in heart failure care delivery, benefiting the patients, providers, and payer...

Artificial Intelligence and Mechanical Circulatory Support.

Heart failure clinics
Advances in machine learning algorithms and computing power have fueled a rapid increase in artificial intelligence research in health care, including mechanical circulatory support. In this review, we highlight the needs for artificial intelligence ...