Risk Management

A governance model for the application of AI in health care.

As the efficacy of artificial intelligence (AI) in improving aspects of healthcare delivery is increasingly becoming evident, it becomes likely that AI will be incorporated in routine clinical care in the near future. This promise has led to growing focus and investment in AI medical applications both from governmental organizations and technological companies. However, concern has been expressed ...

Systematic Review of Privacy-Preserving Distributed Machine Learning From Federated Databases in Health Care.

Big data for health care is one of the potential solutions to deal with the numerous challenges of health care, such as rising cost, aging population, precision medicine, universal health coverage, and the increase of noncommunicable diseases. However, data centralization for big data raises privacy and regulatory concerns.Covered topics include (1) an introduction to privacy of patient data and d...

The use of a centrifuge-free RABiT-II system for high-throughput micronucleus analysis.

The cytokinesis-block micronucleus (CBMN) assay is considered to be the most suitable biodosimetry method for automation. Previously, we automated thi...

"A patient like me" - An algorithm-based program to inform patients on the likely conditions people with symptoms like theirs have.

To date, consumer health tools available over the web suffer from serious limitations that lead to low quality health- related information. While heal...

Artificial Intelligence-Based Classification of Breast Lesions Imaged With a Multiparametric Breast MRI Protocol With Ultrafast DCE-MRI, T2, and DWI.

OBJECTIVES: We investigated artificial intelligence (AI)-based classification of benign and malignant breast lesions imaged with a multiparametric bre...

A Machine Learning Strategy for Autism Screening in Toddlers.

OBJECTIVE: Autism spectrum disorder (ASD) screening can improve prognosis via early diagnosis and intervention, but lack of time and training can dete...

Is Watson for Oncology Unreasonably Dangerous?: Making A Case for How to Prove Products Liability Based on a Flawed Artificial Intelligence Design.

Artificial intelligence (AI) machines hold the world's curiosity captive. Futuristic television shows like West World are set in desert lands against ...

Legal, regulatory, and ethical frameworks for development of standards in artificial intelligence (AI) and autonomous robotic surgery.

BACKGROUND: This paper aims to move the debate forward regarding the potential for artificial intelligence (AI) and autonomous robotic surgery with a ...

Personalized Pancreatic Cancer Management: A Systematic Review of How Machine Learning Is Supporting Decision-making.

This review critically analyzes how machine learning is being used to support clinical decision-making in the management of potentially resectable pan...

Data Profiling in Support of Entity Resolution of Multi-Institutional EHR Data.

Information Quality (IQ) is a core tenant of contemporary data management practices. Across many disciplines and industries, it has become a necessary...

Development of a robotic upper limb assessment to configure a serious game.

BACKGROUNDROBiGAME project aims to implement serious games on robots to rehabilitate upper limb (UL) in stroke patients. The serious game characterist...

A Cell Segmentation/Tracking Tool Based on Machine Learning.

The ability to gain quantifiable, single-cell data from time-lapse microscopy images is dependent upon cell segmentation and tracking. Here, we presen...

Can a Robot Bring Your Life Back? A Systematic Review for Robotics in Rehabilitation.

Stroke is a leading cause of disability in the world and the use of robots in rehabilitation has become increasingly common. The Fourth Industrial Rev...

Machine learning methods for automatic pain assessment using facial expression information: Protocol for a systematic review and meta-analysis.

INTRODUCTION: Prediction of pain using machine learning algorithms is an emerging field in both computer science and clinical medicine. Several machin...

Evaluation of a protocol for reducing the microbial contamination of dental unit water.

Biofilm on dental unit waterlines can spread microbial contamination in the water. The aim of this study was to investigate microbial contamination of...

Efficiency Improvement in a Busy Radiology Practice: Determination of Musculoskeletal Magnetic Resonance Imaging Protocol Using Deep-Learning Convolutional Neural Networks.

The purposes of this study are to evaluate the feasibility of protocol determination with a convolutional neural networks (CNN) classifier based on sh...

Using machine learning for sequence-level automated MRI protocol selection in neuroradiology.

Incorrect imaging protocol selection can lead to important clinical findings being missed, contributing to both wasted health care resources and patie...

Automatic Determination of the Need for Intravenous Contrast in Musculoskeletal MRI Examinations Using IBM Watson's Natural Language Processing Algorithm.

Magnetic resonance imaging (MRI) protocoling can be time- and resource-intensive, and protocols can often be suboptimal dependent upon the expertise o...

Deep Learning Electronic Cleansing for Single- and Dual-Energy CT Colonography.

Electronic cleansing (EC) is used for computational removal of residual feces and fluid tagged with an orally administered contrast agent on CT colono...

[Robotization of the human under the law].

The robotization of the human implies a more or less intimate hybridization with the machine. When it participates in the repair of the human being, i...