[HTML][HTML] Artificial intelligence-enhanced electrocardiography in cardiovascular disease management
The application of artificial intelligence (AI) to the electrocardiogram (ECG), a ubiquitous and
standardized test, is an example of the ongoing transformative effect of AI on cardiovascular …
standardized test, is an example of the ongoing transformative effect of AI on cardiovascular …
[HTML][HTML] Ethical machine learning in healthcare
The use of machine learning (ML) in healthcare raises numerous ethical concerns,
especially as models can amplify existing health inequities. Here, we outline ethical …
especially as models can amplify existing health inequities. Here, we outline ethical …
[HTML][HTML] The role of machine learning in clinical research: transforming the future of evidence generation
Background Interest in the application of machine learning (ML) to the design, conduct, and
analysis of clinical trials has grown, but the evidence base for such applications has not …
analysis of clinical trials has grown, but the evidence base for such applications has not …
A survey on deep learning in medicine: Why, how and when?
New technologies are transforming medicine, and this revolution starts with data. Health
data, clinical images, genome sequences, data on prescribed therapies and results …
data, clinical images, genome sequences, data on prescribed therapies and results …
Prospective evaluation of smartwatch-enabled detection of left ventricular dysfunction
Although artificial intelligence (AI) algorithms have been shown to be capable of identifying
cardiac dysfunction, defined as ejection fraction (EF)≤ 40%, from 12-lead …
cardiac dysfunction, defined as ejection fraction (EF)≤ 40%, from 12-lead …
Application of artificial intelligence to the electrocardiogram
Artificial intelligence (AI) has given the electrocardiogram (ECG) and clinicians reading them
super-human diagnostic abilities. Trained without hard-coded rules by finding often …
super-human diagnostic abilities. Trained without hard-coded rules by finding often …
Deep learning and the electrocardiogram: review of the current state-of-the-art
In the recent decade, deep learning, a subset of artificial intelligence and machine learning,
has been used to identify patterns in big healthcare datasets for disease phenotyping, event …
has been used to identify patterns in big healthcare datasets for disease phenotyping, event …
Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review
Background The electrocardiogram (ECG) is one of the most commonly used diagnostic
tools in medicine and healthcare. Deep learning methods have achieved promising results …
tools in medicine and healthcare. Deep learning methods have achieved promising results …
Wild-time: A benchmark of in-the-wild distribution shift over time
Distribution shifts occur when the test distribution differs from the training distribution, and
can considerably degrade performance of machine learning models deployed in the real …
can considerably degrade performance of machine learning models deployed in the real …
[HTML][HTML] Evaluation and mitigation of racial bias in clinical machine learning models: scoping review
Background Racial bias is a key concern regarding the development, validation, and
implementation of machine learning (ML) models in clinical settings. Despite the potential of …
implementation of machine learning (ML) models in clinical settings. Despite the potential of …