AIMC Topic: Artificial Intelligence

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Methodological Challenges of Deep Learning in Optical Coherence Tomography for Retinal Diseases: A Review.

Translational vision science & technology
Artificial intelligence (AI)-based automated classification and segmentation of optical coherence tomography (OCT) features have become increasingly popular. However, its 3-dimensional volumetric nature has made developing an algorithm that generaliz...

Can exascale computing and explainable artificial intelligence applied to plant biology deliver on the United Nations sustainable development goals?

Current opinion in biotechnology
Human population growth and accelerated climate change necessitate agricultural improvements using designer crop ideotypes (idealized plants that can grow in niche environments). Diverse and highly skilled research groups must integrate efforts to br...

Ethical concerns around use of artificial intelligence in health care research from the perspective of patients with meningioma, caregivers and health care providers: a qualitative study.

CMAJ open
BACKGROUND: As artificial intelligence (AI) approaches in research increase and AI becomes more integrated into medicine, there is a need to understand perspectives from members of the Canadian public and medical community. The aim of this project wa...

[The value of artificial and human intelligence - the example of bone scintigraphy].

Magyar onkologia
We present a possible method of Artificial Intelligence (AI) based applications that can effectively filter noise-sensitive bone scintigraphy images. The use of special AI, based on preliminary examinations, allows us to significantly reduce study ti...

Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT: external validation and clinical utility for resident training.

European radiology
PURPOSE: This study aimed to validate a deep learning model's diagnostic performance in using computed tomography (CT) to diagnose cervical lymph node metastasis (LNM) from thyroid cancer in a large clinical cohort and to evaluate the model's clinica...

Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations.

European radiology
Artificial intelligence (AI) has the potential to significantly disrupt the way radiology will be practiced in the near future, but several issues need to be resolved before AI can be widely implemented in daily practice. These include the role of th...

A Review of Algorithm & Hardware Design for AI-Based Biomedical Applications.

IEEE transactions on biomedical circuits and systems
This paper reviews the state of the arts and trends of the AI-Based biomedical processing algorithms and hardware. The algorithms and hardware for different biomedical applications such as ECG, EEG and hearing aid have been reviewed and discussed. Fo...

Development of a system based on artificial intelligence to identify visual problems in children: study protocol of the TrackAI project.

BMJ open
INTRODUCTION: Around 70% to 80% of the 19 million visually disabled children in the world are due to a preventable or curable disease, if detected early enough. Vision screening in childhood is an evidence-based and cost-effective way to detect visua...

Assessing and Mitigating Bias in Medical Artificial Intelligence: The Effects of Race and Ethnicity on a Deep Learning Model for ECG Analysis.

Circulation. Arrhythmia and electrophysiology
BACKGROUND: Deep learning algorithms derived in homogeneous populations may be poorly generalizable and have the potential to reflect, perpetuate, and even exacerbate racial/ethnic disparities in health and health care. In this study, we aimed to (1)...