Artificial intelligence in cardiology

Itchhaporia, Dipti

doi:10.1016/j.tcm.2020.11.007

Cited by 46 publications

(32 citation statements)

References 32 publications

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“…The results further support the policy-makers in allocation of resources for establishment of comprehensive systems of integrated health IT aiming at simplification of analytics of ML. Dipti Itchhaporia [ 11 ] analyzed the existing application and state of machine learning approaches and artificial intelligence in cardiovascular medicine. The effects of emerging technologies on cardiovascular medicine are emphasized for providing understanding to the clinical practice and to find probable patient assistances.…”

Section: Related Workmentioning

confidence: 99%

Big Data, Extracting Insights, Comprehension, and Analytics in Cardiology: An Overview

Ali

Zhang

et al. 2021

Journal of Healthcare Engineering

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Healthcare system facilitates the treatment of patients with the support of wearable, smart, and handheld devices, as well as many other devices. These devices are producing a huge bulk of data that need to be moulded for extracting meaningful insights from them for the useful use of researchers and practitioners. Various approaches, methods, and tools are in use for doing so and to extract meaningful information in the field of healthcare. This information is being used as evidence to further analyze the data for the early care of patient and to devise treatment. Early care and treatment can facilitate healthcare and the treatment of the patient and can have immense potentiality of dropping the care cost and quality refining of care and can decrease waste and chances of error. To facilitate healthcare in general and cardiology in specific, the proposed study presents an overview of the available literature associated with big data, its insights, and analytics. The presented report will help practitioners and researchers to devise new solutions for early care in healthcare and in cardiology.

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Section: Related Workmentioning

confidence: 99%

Big Data, Extracting Insights, Comprehension, and Analytics in Cardiology: An Overview

Ali

Zhang

et al. 2021

Journal of Healthcare Engineering

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“…Artificial intelligence refers to the all-encompassing ability of mathematical algorithms to train machines to mimic human intelligence. With the use of programmed algorithms, machines are able to complete tasks, execute decisions, and recognize images [ 1 ]. Within AI, machine learning is a subset ( Figure 1 ) that identifies patterns among big datasets.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is most effective for cardiovascular imaging, such as echocardiography, angiography, and cardiac magnetic resonance. This is especially true as deep learning has the ability to parse through insignificant or noisy data [ 1 ]. Although deep learning is useful with image recognition, it is limited insofar as its algorithm cannot be efficiently applied for all types of datasets.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Advances in the World of Cardiovascular Imaging

Patel

Makaryus

2022

Healthcare

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The tremendous advances in digital information and communication technology have entered everything from our daily lives to the most intricate aspects of medical and surgical care. These advances are seen in electronic and mobile health and allow many new applications to further improve and make the diagnoses of patient diseases and conditions more precise. In the area of digital radiology with respect to diagnostics, the use of advanced imaging tools and techniques is now at the center of evaluation and treatment. Digital acquisition and analysis are central to diagnostic capabilities, especially in the field of cardiovascular imaging. Furthermore, the introduction of artificial intelligence (AI) into the world of digital cardiovascular imaging greatly broadens the capabilities of the field both with respect to advancement as well as with respect to complete and accurate diagnosis of cardiovascular conditions. The application of AI in recognition, diagnostics, protocol automation, and quality control for the analysis of cardiovascular imaging modalities such as echocardiography, nuclear cardiac imaging, cardiovascular computed tomography, cardiovascular magnetic resonance imaging, and other imaging, is a major advance that is improving rapidly and continuously. We document the innovations in the field of cardiovascular imaging that have been brought about by the acceptance and implementation of AI in relation to healthcare professionals and patients in the cardiovascular field.

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“…The application of AI has expanded prominently in the medical field due to advances in computing power, learning algorithms, data storage, and the availability of large-high-quality data sourced from electronic medical records and wearable health trackers [1,2]. Although its adoption is still in early phases, AI has been extensively used across many fields in medicine such as radiology [6,7], cardiology [8][9][10][11], dermatology [12][13][14][15], ophthalmology [16,17], neurology [18,19], oncology [20,21], gastroenterology [22,23], and respiratory medicine [24]. Some examples of clinical applications that have been approved by the US Food and Drug Administration (FDA) include Arterys for cardiac magnetic resonance image analysis, Idx for detection of diabetic retinopathy, and Mam-moScreen for breast cancer screening [25,26].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in arthroplasty

2021

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Artificial intelligence (AI) is altering the world of medicine. Given the rapid advances in technology, computers are now able to learn and improve, imitating humanoid cognitive function. AI applications currently exist in various medical specialties, some of which are already in clinical use. This review presents the potential uses and limitations of AI in arthroplasty to provide a better understanding of the existing technology and future direction of this field.Recent literature demonstrates that the utilization of AI in the field of arthroplasty has the potential to improve patient care through better diagnosis, screening, planning, monitoring, and prediction. The implementation of AI technology will enable arthroplasty surgeons to provide patient-specific management in clinical decision making, preoperative health optimization, resource allocation, decision support, and early intervention. While this technology presents a variety of exciting opportunities, it also has several limitations and challenges that need to be overcome to ensure its safety and effectiveness.

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Artificial intelligence in cardiology

Cited by 46 publications

References 32 publications

Big Data, Extracting Insights, Comprehension, and Analytics in Cardiology: An Overview

Big Data, Extracting Insights, Comprehension, and Analytics in Cardiology: An Overview

Artificial Intelligence Advances in the World of Cardiovascular Imaging

Artificial intelligence in arthroplasty

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