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September 1, 2022

AI-driven algorithm funded by British Heart Foundation helps diagnose heart attacks

There has been an increase in the adoption of new AI technologies in healthcare such as the one for diagnosing heart attacks funded by the British Heart Foundation.

By GlobalData

The Covid-19 pandemic has sparked an increased interest in innovation and the adoption of new technologies such as artificial intelligence (AI) and machine learning (ML) in the healthcare industry. GlobalData forecasts that the market for AI platforms for the entire healthcare industry will reach $4.3bn by 2024, up from $1.5bn in 2019. This will be driven by the use of AI by healthcare providers and payers, which is forecast to reach $2.9bn by 2024. There are a lot of areas in healthcare where AI can be applied—data management, remote surgery, diagnostic and procedural AI assistants, drug discovery, and clinical trial design—to improve the current workflow. Additionally, its use in the healthcare space is expected to continue to increase in the next five years.

Recently, an algorithm using AI was found to help diagnose heart attacks in women more quickly and accurately. Research for developing an AI-based tool was funded by the British Heart Foundation (BHF). The researchers from the University Of Edinburgh obtained data from 10,038 people, 48% of whom were women, to develop an AI-based tool named ‘CoDE-ACS’ that can help healthcare providers diagnose and detect heart attacks. This tool is able to combine gender, age observations, electrocardiogram (ECG) findings, blood test results and the medical history of patients when they arrive at the hospital. An AI-driven algorithm was found to have the potential to exclude heart attacks with 99.5% accuracy, confirming that the people are safe. Additionally, it identified the people who needed to stay at the hospital for future tests with 83.7% accuracy.

The rapid surge of AI-powered platforms in traditional healthcare will further improve the quality of care. AI will greatly contribute to reducing the time to diagnosis and treatment and will make communication between medical personnel and patients faster and easier.

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