AI in Cardiac Care: Can It Outpace Your Cardiologist?
By Arunima Rajan
Artificial intelligence is slowly finding space inside Indian hospitals. Its biggest trial by fire is in cardiac care, where doctors are turning to machines to read scans and flag risks. The technology brings hope, but also unease. Are cardiologists ready to depend on it? Where does the patient’s data go once it is stored on overseas servers?
Kiran, a 62-year-old engineer who worked in the Gulf, spent close to ten days in hospital recently. He was admitted with palpitations, but cardiologists could not identify the cause. A CT calcium score, an angiogram and several other tests offered little clarity. Concerned about his health, Kiran reflects: “I wish there was a predictive system that could find cardiac issues early.” It is precisely this gap that India’s multi-speciality hospitals are beginning to address, turning to artificial intelligence to support diagnosis in cardiac care.
According to Dr. Ravi Prakash, senior consultant cardiologist at PSRI Hospital, Delhi, the hospital uses AI-powered ECG analysis, echocardiography interpretation, and CT angiography image processing to detect subtle abnormalities that might be missed by the human eye. "AI helps flag high-risk patients early and prioritise them for intervention. Clinically, we have observed faster diagnosis times, sometimes reduced from hours to minutes, and a measurable reduction in readmissions for heart failure because treatment plans are optimised earlier," he explains.
What are the AI applications that are relevant for cardiology? "The applications of AI that could be relevant to cardiology are AI-assisted echocardiography interpretation, which can provide a rapid assessment of structural abnormalities," states Pankaj Chandna, co-founder of Vaidam Health. "Predictive analytics, which incorporate clinical and lifestyle data to identify at-risk patients. AI-enabled ECG analysis, for the advancement of detecting early arrhythmias or ischaemic changes. While there are limited formal statistics on these tools, anecdotal observations from the industry suggest that many of India's top private hospitals may already be utilising at least one of these in routine cardiology practice, with AI-assisted echo reading and risk prediction platforms presumed to be most commonplace."
But can AI models today reliably predict cardiac events in high-risk patients, and how do hospitals validate their accuracy before clinical use? "The predictive models are promising but not perfect," observes Dr. Prakash. "We validate them against historical patient data and run pilot trials under human supervision before integrating them into routine care. Any alert from AI is always cross-checked with clinical judgment before acting on it."
What measurable improvements have hospitals reported in diagnostic accuracy or patient outcomes after implementing AI in cardiology? Chandna remarks that in theory, AI could minimise variability in diagnoses, decrease reporting times, and detect disease early, thus affording patients early intervention that may prolong life and enhance health-related quality of life. Clinician feedback, though informal, indicates some institutions observe quicker time-to-diagnosis or at least some uptick in early detection rates, but again, we do not yet have the data nationally to properly quantify this.
Cardiac care relies on sensitive patient data. How do they address concerns around storing and processing this data, especially when much of the AI infrastructure is on foreign cloud platforms?
Dr. Prakash emphasises that data privacy is non-negotiable. "We use strong encryption, anonymise patient identifiers before uploading, and ensure our agreements with cloud providers meet both Indian IT regulations and global medical data protection standards. Where possible, we are shifting towards on-premise AI processing to minimise exposure of patient data outside India," he explains.
What role should cardiologists play in overseeing AI-driven decisions, especially when AI outputs challenge traditional clinical judgment?
"Cardiologists must act as the final gatekeepers. AI can inform, but it cannot replace the contextual knowledge, ethical considerations, and patient-specific nuances a doctor brings. If AI recommendations conflict with our judgment, we investigate further rather than simply override or accept them," Dr. Prakash maintains.
Across the world, AI is moving beyond information crunching to systems that can plan and act on their own. In the next three to five years, what difference could this shift make to cardiac care?"Autonomous AI could handle routine monitoring, medication adjustments, and early warning alerts without requiring constant doctor input. This will free cardiologists to focus on complex cases. However, human oversight will still be critical to ensure safety and patient trust," Dr. Prakash points out.
India still does not have clear guidelines for advanced medical AI. What safeguards does he think are essential before high-autonomy systems are used in patient care?"We need a regulatory framework that mandates clinical validation in Indian patient populations, independent audits of AI algorithms, and transparent disclosure of decision-making logic to clinicians. Without this, there is a risk of blind reliance on AI outputs that may not suit local contexts," he stresses.
Most medical AI in India comes from abroad. Does Dr. Prakash see a case for building homegrown models trained on Indian cardiac data, and what challenges stand in the way?"Absolutely. Indian patients often present different disease patterns, comorbidities, and risk factors compared to Western populations. Indigenous AI would improve accuracy. The main barriers are lack of large, clean, standardised datasets and funding for AI development in healthcare," he acknowledges.
Chandna elaborates on Vaidam Health's perspective: "At Vaidam Health, we connect patients from over 100 countries to leading NABH- and JCI-accredited hospitals, and we've observed that partner cardiology departments increasingly list AI-enabled echo and predictive risk modelling among their differentiators when attracting international patients."
From his data, how does the use of AI in cardiology at Indian private hospitals stack up against leading medical tourism hubs, in terms of technology and outcomes?
Chandna observes that given sufficient infrastructure and trained personnel, Indian hospitals might achieve the sophistication of AI implementations in established medical tourism countries like Singapore or Thailand. Some recent hospital technology implementations indicate that some of the high-end Indian hospitals are already working at the same technological level, with an operational cost advantage that adds to their attractiveness for overseas patients. But he cautions that in theory, the ability of AI to detect complications earlier could mean fewer days in the hospital and fewer readmissions. Some hospitals are claiming to see slight decreases in average lengths of stay, with even fewer readmissions after elective cardiac procedures, such as heart failure, may be lower, but these findings are not confirmed by any large cohort studies at this time.
He continues that AI-assisted cardiac care is strongly positioned to attract patients who are seeking precision and preventive treatment options. Early indications reflect demand from patients in the Middle East, Africa, and South Asia, particularly for angioplasty, valve procedures, and arrhythmia care supported by AI, while screening through AI risk models for preventive measures may also present a niche opportunity for wellness-oriented medical travellers. "Our own facilitation data shows a growing segment of inbound patients actively enquiring about hospitals that integrate AI in pre-procedure screening and post-operative monitoring, especially for complex interventions where accuracy and recovery outcomes are critical."
In the hospitals Chandna tracks, how do the costs of AI-assisted cardiac procedures compare with conventional ones?
"Globally, AI-assisted diagnostics can reduce overall treatment costs by minimising complications and preventing needless procedures, even if they may have higher upfront equipment costs. Although these findings are still preliminary, some administrators in India think that these efficiencies could eventually make AI-based cardiology cost-neutral or perhaps cost-saving," Chandna reveals.
What are the main operational or infrastructure hurdles hospitals face when trying to scale AI in cardiology? Theoretical models suggest that AI in healthcare demands strong IT systems, secure data practices, and clinical readiness. But hospital leaders say the real hurdles lie in fitting AI into old systems, keeping up with changing privacy rules, and ensuring staff are trained well and trained often.
If AI becomes capable of rapid, near-autonomous medical decision-making, what steps should private hospitals take now to ensure readiness, both technologically and ethically? "Hospitals must invest in secure infrastructure, train staff to work with AI tools, and establish ethical review boards to oversee autonomous decision-making. Building patient education programmes is also essential so that people understand AI's role in their care and maintain confidence in the system," advises Dr. Prakash.
Chandna points out that India could take to AI in cardiology faster once early results are proven and costs ease. Market signals over the past two to three years already show steady growth. Over the next five years, he expects adoption to rise further as software becomes cheaper, policies turn more supportive, and insurance cover expands.
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