AI/ML to Unlock Heart Ailments
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AI/ML has brought tremendous benefits to the healthcare industry. Like many other applications, it can be used for Cardiac health as well. This is foreseen to impact a range of beneficiaries including patients, hospitals, medical staff, commissioners and so, the entire healthcare system.
One can imagine the complexities involved in analysis of radiology data for Cardiac health. Right from wall thickness, blockages and pumping pressure analysis. The deep learning technologies with right application can bring some relief to all above stated stakeholders.
With the involvement of AI it is postulated that physicians will start working more efficiently. Medical AI will also help them obtain more sophisticated information from imaging, and find patterns in available data sources that are too complex for the human brain.
Some examples of AI in non-invasive cardiac imaging include:
Referring the right cardiac imaging test at the right time for the patient.
Scheduling/prioritizing the medical procedures in an appropriate sequence.
Image acquisition techniques including AI optimized image reconstruction, quality control, recognition & correction of artifacts
Cardiac image analysis: image recognition, segmentation & quantification
Study reporting
Clinical decision support systems
Estimation of prognosis
However, many challenges remain and need to be proactively addressed such as:
Multi-disciplinarity is the key to success. Simply handing over data to the data scientist is not sufficient.
Technicians responsible for imaging need to be trained to become data experts/AI enablers and AI experts.
Solutions with regards to transparency & data safeguarding issues like privacy, ethics, and commercial partnerships need to be found.
Patient and public support, 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.
These challenges need to be addressed proactively in order to pace up AI developments in cardiac imaging.
Apart from all of this, adequate research is also required to demonstrate the real-world benefits of AI solutions to the public & patients in terms of clinical effectiveness. Economic evaluations also need to be carried out.
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