AI in Cardiac Care: Can It Outpace Your Cardiologist?
By Arunima Rajan
Artificial intelligence is slowly finding space inside Indian hospitals. 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?
Despite spending nearly ten days in hospital after being admitted for palpitations, cardiologists treating Kiran, a 62-year-old engineer, could not identify the cause. A CT calcium score, an angiogram and several other tests offered little clarity. Kiran says: “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 the human eye might miss. “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.
AI in cardiology
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. Hospitals are now using AI-enabled ECG analysis, to detect arrhythmias or ischaemic changes early. While there are limited formal statistics on these tools, industry observers 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 reliably predict cardiac events in high-risk patients? 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 we act 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 its quality. Clinician feedback, though informal, indicates some institutions observe quicker time-to-diagnosis or at least some uptick in early detection rates, but again, there is no nation-wide data to accurately quantify this.
Data security
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-premises AI processing to minimise exposure of patient data outside India,” he explains.
Who has the final say?
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 affirms.
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 says.
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.
India-specific challenges
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, co-
morbidities 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. We’ve observed that partner cardiology departments increasingly list AI-enabled echo and predictive risk modelling among their differentiators when attracting international patients.”
Going by his data, how does the use of AI in cardiology at Indian private hospitals stack up against major medical tourism hubs abroad in terms of technology and outcomes?
Chandna observes that given sufficient infrastructure and trained personnel, Indian hospitals might achieve the sophistication of AI implementation in such established places as 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 cost advantage that increases their appeal for foreign patients. But he says 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 a slight decrease in average length of stay, with even fewer readmissions after elective cardiac procedures, may be lower, but these findings are not confirmed by any large cohort studies at this time.
Chandna adds that AI-assisted cardiac care is positioned to attract patients who are seeking precision and preventive treatment options. Early indications reflect demand from patients in West and South Asia and Africa, particularly for angioplasty, valve procedures and arrhythmia care. 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.”
The cost factor
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 initial cost is higher for the equipment. Although these findings are still preliminary, some administrators in India think these efficiencies could eventually make AI-based cardiology cost-neutral or perhaps cost-saving,” says Chandna.
What are the main operational or infrastructure hurdles hospitals face when trying to scale up 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, 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, in terms of technology and ethics? “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 AI adoption to grow as software becomes cheaper, policies turn more supportive, and insurance cover expands.
Sidharth Srinivasan, Lupin
Where is the investment in AI for cardiac care being directed within the hospital? “Hospitals are putting AI budgets where seconds save lives and consistency saves costs.”
Most hospitals are directing AI investments toward diagnostics, acute monitoring, and digital therapeutics areas where delays or variability can have real clinical and financial impact:
Imaging & diagnostics: AI-enhanced echo and cardiac CT reduce scan processing from ~30 minutes to under 5, improving consistency and reporting speed.
Triage & monitoring: ICU early-warning tools powered by AI are up to five times more accurate than standard alarms at detecting instability, reducing false alarms and missed events.
Workflow-level support: AI tools are being layered into triage workflows and emergency prioritization to stratify heart-failure and MI patients more effectively.
Survey evidence: The Philips Future Health Index found that 40% of cardiology leaders are already investing in AI, and over 80% expect to be using it for decision support within three years.
In practice, AI investments are being channeled into tools that integrate into clinical workflows, rather than stand-alone solutions.
How are clinical and operational teams being trained to use these AI tools effectively? “Hospitals are learning that AI adoption is 10% technology and 90% trust.”
A 2023 HIMSS study found that 72% of hospitals cite “staff readiness” as the main barrier to AI adoption—so many systems are now baking AI modules into CME, simulation labs, and onboarding. Hospitals are therefore taking a multi-pronged approach to build capability and confidence:
Integrated training modules: Major imaging vendors now include AI training in their onboarding for echo, CT, and MRI systems, ensuring clinicians practice with real-world cases before live use.
Case-based CME workshops: Cardiology societies and hospital groups in India and globally are running CME-accredited workshops where AI outputs are compared with gold-standard reads, improving clinician confidence.
Simulation and shadow mode: Many hospitals deploy AI tools in “shadow” mode initially producing outputs alongside clinicians’ own assessments without influencing decisions before switching to live decision support.
Operational staff training: Nurses and administrators are trained on interpreting AI-driven dashboards, escalation protocols, and alert thresholds, ensuring smooth workflow handoffs.
Cross-disciplinary adoption: Training isn’t confined to doctors radiology technicians, cath lab staff, and ICU nurses all get role-specific training on how AI outputs affect their daily work.
A 2022 KPMG survey found that 81% of healthcare executives believe successful AI adoption depends more on workflow integration and clinician trust than on algorithmic accuracy. Hospitals that treat AI onboarding as clinical change management—not just IT deployment—are reporting smoother adoption and faster uptake.
What evidence or results demonstrate that the technology is delivering meaningful outcomes? “The strongest proof today is not in futuristic trials, but in how AI is already making care faster, safer, and more consistent.”
Early but compelling wins are already shifting expectations:
Efficiency & consistency: Echo quantification time drops from ~30 minutes to under 5, with reduced inter-reader variability across centers.
Faster, safer acute care: AI-powered ECG has been tied to shorter door-to-balloon times; ICU alert tools detect instability with five-fold greater accuracy than legacy systems.
Smarter decision-making: AI stratification is cutting down unnecessary admissions and redundant angiograms, improving care value.
Stronger patient engagement (LDH): On Lupin Digital Health’s Lyfe platform, each patient logs over 100 health data points monthly, generating real-world evidence that feeds back to doctors in near real time.
Clinical confidence (LDH): Today, Lyfe is trusted by 1,000+ cardiologists across 60 hospitals, who use its AI nudges to keep patients aligned to protocols between visits.
System-level ROI: Deloitte reports that hospitals deploying AI in clinical decision support have already realized 10–15% cost reductions in targeted service lines largely through fewer readmissions and shorter lengths of stay.
While long-term outcome data (e.g., mortality, rehospitalization) is still emerging, AI’s value today lies in boosting efficiency, generating trusted real-world evidence, and closing the gap between clinic and home an encouraging foundation for the future.
What regulatory challenges have been encountered in deploying AI for cardiac care? “The good news is that India’s regulatory environment is evolving quickly — the challenge is simply that hospitals and innovators must keep pace.”
Regulatory frameworks are catching up with rapidly advancing AI:
Framework ambiguity: Device pathways like CDSCO’s SaMD regulations are still maturing, especially for adaptive algorithms.
Dynamic models: Regulators prefer “frozen” versions of software, making oversight of evolving models a challenge.
Validation expectations: Clinical trials for AI tools don't fit traditional RCT standards, leaving developers to define acceptable evidence.
Data privacy: India’s Digital Personal Data Protection Act (2023) makes patient consent and data-sharing protocols essential for approval.
Global misalignment: FDA, EU MDR, and Indian approvals don’t always align, slowing cross-border tool deployment.
Positively, initiatives like the National Digital Health Mission and updated SaMD guidelines are laying clearer paths. But hospitals must still invest in both technology and regulatory strategy to succeed.
How has the use of AI changed the experience and outcomes for patients? “On Lupin’s Lyfe platform, AI doesn’t feel like technology it feels like a companion that keeps patients on track long after discharge.”
For patients, AI isn’t visible it’s about support, consistency, and confidence:
Greater safety: AI-enabled monitoring spots early arrhythmias and signs of deterioration, reducing anxiety about missed events.
Less invasive care: Smarter risk tools reduce unnecessary procedures, sparing patients the risks and discomfort.
Post-discharge engagement: On Lyfe, 94% of patients complete their cardiac rehab, and more than 95% follow their doctor’s prescribed protocols, in contrast to typical 50%+ dropout rates seen elsewhere.
Emotional reassurance: Patients often say AI platforms feel like “someone is watching over me,” providing comfort between visits.
Better downstream outcomes: Improved BP control, medication adherence, and long-term engagement all shown to correlate with better survival and fewer readmissions even before definitive mortality data is in.
AI isn’t replacing clinicians it’s elevating the patient journey from episodic to continuous, compassionate, and measurable.
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