Medical Ethics / Professional Responsibility

Mapping ethico-legal principles for the use of artificial intelligence in gastroenterology.

The rapid development of artificial intelligence (AI) and digital health raise concerns about equitable access to innovative interventions, appropriate use of health data and privacy, inclusiveness, bias and discrimination, and even changes to the clinician-patient relationship. This article outlines a number of ethical and legal issues when examining the use of AI in gastroenterology. Substantive...

[The Clinical Effectiveness of Neural Network-based Boundary Recognition of Upper Abdominal Organs on CT Images].

OBJECTIVE: To assess the clinical effectiveness of boundary recognition of upper abdomen organs on CT images based on neural network model and the combination of different slices.

Automatic quality assessment for 2D fetal sonographic standard plane based on multitask learning.

The quality control of fetal sonographic (FS) images is essential for the correct biometric measurements and fetal anomaly diagnosis. However, quality...

Image quality improvement of single-shot turbo spin-echo magnetic resonance imaging of female pelvis using a convolutional neural network.

We have developed a deep learning-based approach to improve image quality of single-shot turbo spin-echo (SSTSE) images of female pelvis. We aimed to ...

A critical perspective on guidelines for responsible and trustworthy artificial intelligence.

Artificial intelligence (AI) is among the fastest developing areas of advanced technology in medicine. The most important qualia of AI which makes it ...

DNN-Dom: predicting protein domain boundary from sequence alone by deep neural network.

MOTIVATION: Accurate delineation of protein domain boundary plays an important role for protein engineering and structure prediction. Although machine...

Investigation of Low-Dose CT Lung Cancer Screening Scan "Over-Range" Issue Using Machine Learning Methods.

Low-dose computed tomography (CT) lung cancer screening is recommended by the US Preventive Services Task Force for high lung cancer-risk populations....

Recurrent Deep Network Models for Clinical NLP Tasks: Use Case with Sentence Boundary Disambiguation.

Although a number of foundational natural language processing (NLP) tasks like text segmentation are considered a simple problem in the general Englis...

Impact of De-Identification on Clinical Text Classification Using Traditional and Deep Learning Classifiers.

Clinical text de-identification enables collaborative research while protecting patient privacy and confidentiality; however, concerns persist about t...

ConDo: protein domain boundary prediction using coevolutionary information.

MOTIVATION: Domain boundary prediction is one of the most important problems in the study of protein structure and function. Many sequence-based domai...

Skeletal bone age assessments for young children based on regression convolutional neural networks.

Pediatricians and pediatric endocrinologists utilize Bone Age Assessment (BAA) for in-vestigations pertaining to genetic disorders, hormonal complicat...

Boundary-aware Semi-supervised Deep Learning for Breast Ultrasound Computer-Aided Diagnosis.

Breast ultrasound (US) is an effective imaging modality for breast cancer diagnosis. US computer-aided diagnosis (CAD) systems have been developed for...

Towards Data-Driven Autonomous Robot-Assisted Physical Rehabilitation Therapy.

Task-oriented therapy consists of three stages: demonstration, observation and assistance. While demonstration using robots has been extensively studi...

DeepDom: Predicting protein domain boundary from sequence alone using stacked bidirectional LSTM.

Protein domain boundary prediction is usually an early step to understand protein function and structure. Most of the current computational domain bou...

Automatic Organ Segmentation for CT Scans Based on Super-Pixel and Convolutional Neural Networks.

Accurate segmentation of specific organ from computed tomography (CT) scans is a basic and crucial task for accurate diagnosis and treatment. To avoid...

Fetal MRI Synthesis via Balanced Auto-Encoder Based Generative Adversarial Networks.

Machine learning approaches for image analysis require large amounts of training imaging data. As an alternative, the use of realistic synthetic data ...

A Fully Convolutional Deep Neural Network for Lung Tumor Boundary Tracking in MRI.

Delineation of lung tumor from adjacent tissue from a series of magnetic resonance images (MRI) poses many difficulties due to the image similarities ...

De-identification of patient notes with recurrent neural networks.

OBJECTIVE: Patient notes in electronic health records (EHRs) may contain critical information for medical investigations. However, the vast majority o...

Medical image segmentation via atlases and fuzzy object models: Improving efficacy through optimum object search and fewer models.

PURPOSE: Statistical object shape models (SOSMs), known as probabilistic atlases, are popular in medical image segmentation. They register an image in...

Thinking science with thinking machines: The multiple realities of basic and applied knowledge in a research border zone.

Some scholars dismiss the distinction between basic and applied science as passé, yet substantive assumptions about this boundary remain obdurate in r...