Abstract
Artificial intelligence (AI) using machine learning techniques will change healthcare as we know it. While healthcare AI applications are currently trailing behind popular AI applications, such as personalized web-based advertising, the pace of research and deployment is picking up and about to become disruptive. Overcoming challenges such as patient and public support, transparency over the legal basis for healthcare data use, privacy preservation, technical challenges related to accessing large-scale data from healthcare systems not designed for Big Data analysis, and deployment of AI in routine clinical practice will be crucial. Cardiac imaging and imaging of other body parts is likely to be at the frontier for the development of applications as pattern recognition and machine learning are a significant strength of AI with practical links to image processing. Many opportunities in cardiac imaging exist where AI will impact patients, medical staff, hospitals, commissioners and thus, the entire healthcare system. This perspective article will outline our vision for AI in cardiac imaging with examples of potential applications, challenges and some lessons learnt in recent years.
Highlights
It is almost impossible in modern society to avoid exposure to artificial intelligence (AI) solutions such as facial recognition, speech recognition, spam filters, chatbots, and personalized web-based advertising
AI applications emerge in all non-invasive cardiac imaging modalities and not just cardiovascular magnetic resonance (CMR) covering a range of applications from image classification, image reconstruction, automation in segmentation and quantification and guiding diagnosis and prognosis
In the Multi-Ethnic Study of Atherosclerosis (MESA), Ambale-Venkatesh et al demonstrated the potential of machine learning to develop more accurate cardiovascular risk prediction tools when supplied with clinical information and data from deep cardiac imaging phenotyping, such as CMR, cardiac CT and carotid ultrasound [20]
Summary
While healthcare AI applications are currently trailing behind popular AI applications, such as personalized web-based advertising, the pace of research and deployment is picking up and about to become disruptive Overcoming challenges such as patient and public support, transparency over the legal basis for healthcare data use, privacy preservation, technical challenges related to accessing large-scale data from healthcare systems not designed for Big Data analysis, and deployment of AI in routine clinical practice will be crucial. Many opportunities in cardiac imaging exist where AI will impact patients, medical staff, hospitals, commissioners and the entire healthcare system. This perspective article will outline our vision for AI in cardiac imaging with examples of potential applications, challenges and some lessons learnt in recent years
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