Public Health & Policy

Ethics

Latest AI and machine learning research in ethics for healthcare professionals.

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Robo-investment aversion.

In five experiments (N = 3,828), we investigate whether people prefer investment decisions to be mad...

Automatic detection of non-perfusion areas in diabetic macular edema from fundus fluorescein angiography for decision making using deep learning.

Vision loss caused by diabetic macular edema (DME) can be prevented by early detection and laser pho...

Machine learning at the interface of structural health monitoring and non-destructive evaluation.

While both non-destructive evaluation (NDE) and structural health monitoring (SHM) share the objecti...

Classification of Non-Severe Traumatic Brain Injury from Resting-State EEG Signal Using LSTM Network with ECOC-SVM.

Traumatic brain injury (TBI) is one of the common injuries when the human head receives an impact du...

Convolutional Neural Network Based Approach to in Silico Non-Anticipating Prediction of Antigenic Distance for Influenza Virus.

Evaluation of the antigenic similarity degree between the strains of the influenza virus is highly i...

Ethical and Legal Challenges of Artificial Intelligence in Nuclear Medicine.

Artificial intelligence (AI) in nuclear medicine has gained significant traction and promises to be ...

Considerations for the Ethical Implementation of Psychological Assessment Through Social Media via Machine Learning.

The ubiquity of social media usage has led to exciting new technologies such as machine learning. Ma...

Design and development of a non-contact robotic gripper for tissue manipulation in minimally invasive surgery.

This paper describes the design and testing of a gripper developed for handling of delicate and flex...

Democratizing AI: non-expert design of prediction tasks.

Non-experts have long made important contributions to machine learning (ML) by contributing training...

Rapid reconstruction of highly undersampled, non-Cartesian real-time cine k-space data using a perceptual complex neural network (PCNN).

Highly accelerated real-time cine MRI using compressed sensing (CS) is a promising approach to achie...

Ethics parallel research: an approach for (early) ethical guidance of biomedical innovation.

BACKGROUND: Our human societies and certainly also (bio) medicine are more and more permeated with t...

Adjunctive dental therapies in caries-active children: Shifting the cariogenic salivary microbiome from dysbiosis towards non-cariogenic health.

BACKGROUND: The oral microbiome is a complex assembly of microbial species, whose constituents can t...

Zwitterionic 3D-Printed Non-Immunogenic Stealth Microrobots.

Microrobots offer transformative solutions for non-invasive medical interventions due to their small...

Deep learning algorithm for detection of aortic dissection on non-contrast-enhanced CT.

OBJECTIVES: To develop a deep learning-based algorithm to detect aortic dissection (AD) and evaluate...

ncRDeep: Non-coding RNA classification with convolutional neural network.

A non-coding RNA (ncRNA) is a kind of RNA that is not converted into protein, however, it is involve...

Deep learning from dual-energy information for whole-heart segmentation in dual-energy and single-energy non-contrast-enhanced cardiac CT.

PURPOSE: Deep learning-based whole-heart segmentation in coronary computed tomography angiography (C...

Just data? Solidarity and justice in data-driven medicine.

This paper argues that data-driven medicine gives rise to a particular normative challenge. Against ...

Understanding climate phenomena with data-driven models.

In climate science, climate models are one of the main tools for understanding phenomena. Here, we d...

ZiMM: A deep learning model for long term and blurry relapses with non-clinical claims data.

This paper considers the problems of modeling and predicting a long-term and "blurry" relapse that o...

Automatic detection of acute ischemic stroke using non-contrast computed tomography and two-stage deep learning model.

BACKGROUND AND OBJECTIVE: Currently, it is challenging to detect acute ischemic stroke (AIS)-related...

MRI radiomics for the prediction of recurrence in patients with clinically non-functioning pituitary macroadenomas.

Twelve to 66% of patients with clinically non-functioning pituitary adenoma (NFPA) experience tumor ...

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