Role Ai In Medicine - Hoshino Shiro

Role Ai In Medicine


Ai In Medicine Artificial intelligence (AI) has the potential to revolutionize cardiovascular medicine.In the recent decade, AI has been used to diagnose cancer and diabetic retinopathy. AI has been used in disease diagnosis, treatment, and risk prediction, allowing physicians to analyze results more swiftly and efficiently.



Role Ai In Medicine



Role Ai In Medicine

AI’s involvement in cardiovascular care, especially imaging, will grow over time.

AI medical basics

AI is used in medical diagnosis, therapy, and risk prediction. AI is predicted to:

  • Helping doctors diagnose and treat disease
  • Increase diagnostic accuracy and reduce misdiagnoses
  • Recognizing and supplying correct imaging diagnostic information
  • Big data analysis improves patient prediction findings.
  • Improve medication development and research

In the medical area, notably cardiology, machine learning, deep learning, and cognitive computing are used. [3]

Machine learning (ML) uses massive data to solve complicated problems by discovering interaction patterns. ML lets computers analyse data, find patterns, and make conclusions. This approach is utilized in AI radiology exams. [3,4]

Deep learning (DL) is a type of ML that mimics how the human brain processes data and makes decisions. DL can be employed in 2D- and 3D-speckle-tracking echocardiography, angiography, and cardiac magnetic resonance. DL can be trained to do unsupervised learning tasks, such as drug interaction. [3]

AI in cardiology

AI is mostly used in radiology and imaging. AI can streamline the clinician’s workflow in cardiovascular medicine by analyzing results faster. Several cardiac imaging exams use AI. [4]


Echocardiography is the most common cardiac x-ray. AI can shorten assessment time by automatically calculating echocardiogram results with ML. ML algorithms can recognize endocardial borders and quantify left ventricular volume and function. [5,6]

In the Asch et al study, the ML algorithm was trained to estimate LVEF based on more than 50,000 echocardiogram results, then tested on 99 patients.

Asch et al compared the evaluation results to the average of 3 conventional expert assessments. AI’s ejection fraction value agrees well with physicians’. [6]

Echocardiography can assess cardiac strain, which measures left ventricular regional function by shortening and thickening the myocardium.

Tabassian et al employed cardiac strain to classify maintained ejection fraction heart failure (HFpEF). The AI model predicted hospitalization, exercise intolerance, and left ventricular filling pressure in HFpEF patients. [5]

ML can assess valvular heart disease and suggest treatment. Costa et al. employed DL to segment the mitral valve in PLAX and 4 chamber views. Wang et al are also evaluating ML for mitral input and aortic outflow. ML is in its early stages for this. [5]


ML algorithms can automate and speed up operations, expanding cardiac CT scans.


The DL technique can speed up imaging reconstruction while keeping the diagnostic value of cardiac CT scans by reducing noise data and heart movement abnormalities.

Automatic measurement of CAC score, epicardial adipose tissue (EAT), and cardiac chambers is projected to be integrated into normal clinical reporting, lowering clinicians’ and technicians’ effort.

AI can calculate anatomic and functional stenosis severity in CCTA patients.


CT-FFR is a non-invasive way to diagnose chest discomfort. CT-FFR is a relatively new technology for assessing the heart’s anatomy and function. ML can calculate FFR without CFD and provide prognostic information. [5]


CMR is the gold standard for non-invasive left ventricular ejection fraction and volume evaluation. CMR can determine heart tissue character, which affects disease management. [5,7]

Strain can be evaluated via CMR, like echocardiography, but requires a long study. ML shortens the procedure. [5]

Ruijsink et al observed a strong connection between the CNN algorithm and manual examination of CMR volumes, stresses, filling, and ejection rates.

Winter et al. found that DL could match or exceed human specialists in segmenting right and left ventricle endocardium and epicardium.


Bhuva et al studied 101 individuals with CMR scan:rescan. Volume, mass, and left ventricular ejection fraction measures were also compared.

This study by Bhuva et al compared expert clinician measurements, junior clinician measurements, and AI measurements using the CNN algorithm trained in 599 cases. The three parties’ measurements are accurate. AI analyzes 186x faster than humans. [7]


AI can diagnose, treat, and predict danger in medicine. Machine learning, deep learning, and cognitive computing are AI components. Radiological exams often use AI.

In cardiology, AI can speed up the interpretation of specific outcomes, such as the left ventricular ejection fraction on echocardiography.

CMR can employ cardiac strain as a biomarker to quantify left ventricular ejection fraction and volume non-invasively, although this test is time-consuming. ML algorithms can streamline the procedure with expert-like precision.

To maximize AI’s potential in cardiovascular care, physicians and radiologists must research its development and deployment Role Ai In Medicine

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