Ramaiah University of Applied Sciences, and the London School of Hygiene and Tropical Medicine.
Artificial intelligence (AI) could help India identify people at risk of cardiovascular disease (CVD) earlier and enable more personalised prevention, but the technology is not yet ready to guide routine clinical decisions, according to a recent systematic review by researchers from the Indian Institute of Science (IISc), M.S.
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Most of the models were developed using datasets from the U.S., the U.K., and South Korea, and relied on routinely collected clinical information and machine learning algorithms such as Random Forests, Support Vector Machines, and neural networks. The findings are particularly relevant for India, where cardiovascular disease accounts for nearly one-third of all deaths and often affects people at younger ages than in many other countries. Several Indian institutions have already developed AI-based cardiovascular risk prediction models incorporating locally relevant factors such as smokeless tobacco use, psychosocial stress, and physical inactivity. However, Dr. However, the researchers cautioned that better statistical performance by itself did not establish that an AI model would improve patient care.
Published in BMC Medical Informatics and Decision Making , an open-access journal, the review assessed 30 studies published since 2017 on AI-based models developed to predict future cardiovascular disease among adults without established heart disease. The researchers identified the studies through a systematic search of more than 6,700 records across major scientific databases. The review found that AI models generally performed as well as, and in some cases slightly better than, conventional tools in distinguishing people at higher risk of cardiovascular events from those at lower risk over five to 10 years.
John said such tools needed rigorous independent validation before they were used in primary care or public health programmes. He said that robust external validation, calibration, and assessment of clinical usefulness were necessary before AI tools could guide long-term treatment decisions, such as starting blood pressure- or cholesterol-lowering therapies.
