AI heart risk tools show promise but need more validation, review finds
A review of artificial intelligence (AI) tools designed to predict heart disease has found that while these technologies show considerable promise, they are not yet ready for routine use in clinical settings. The findings carry particular significance for India, where cardiovascular disease accounts for nearly one-third of all deaths and often strikes at younger ages than in many other countries.
The review, which examined a broad range of studies on machine learning models for cardiovascular risk assessment, noted that many AI tools can accurately identify patterns in large datasets that might escape traditional risk calculators. These models are trained on electronic health records, imaging data, and genetic information to estimate a person's likelihood of developing heart disease or experiencing a cardiac event.
However, the authors pointed to several critical gaps that must be addressed before such tools can be safely deployed in hospitals and clinics. One major issue is the lack of external validation: many models perform well on the data they were trained on but fail to maintain accuracy when applied to different populations. This is a particular concern in India, where risk profiles, lifestyle factors, and genetic backgrounds differ significantly from the Western datasets that dominate most AI research.
Another concern is algorithmic bias. If AI models are trained on data that underrepresents certain groups—by age, gender, ethnicity, or socioeconomic status—they may produce inaccurate predictions for those groups. In a diverse country like India, this could lead to both overdiagnosis and underdiagnosis, with serious consequences for patients.
The review also highlighted the issue of interpretability. Many deep-learning models are 'black boxes,' meaning even their developers cannot fully explain why they make a particular prediction. For doctors and patients, understanding the reasoning behind a risk score is essential for making informed decisions. Without clear explanations, clinicians are unlikely to trust AI recommendations, particularly when they contradict established medical knowledge.
Additionally, the researchers emphasised the need for prospective studies. Most existing evidence comes from retrospective analyses, where models are tested on historical data. While these studies are useful for initial evaluation, they cannot capture the real-world complexities of clinical practice, such as changes in patient health, varying data quality, or the impact of AI recommendations on treatment choices and outcomes.
In India, the potential benefits of AI-based heart risk tools are substantial. The country faces a double burden of communicable and non-communicable diseases, and the healthcare system is often stretched thin. AI tools could help screen large populations, prioritise high-risk individuals, and provide decision support in primary care settings where specialist access is limited. They might also help identify younger patients who are at risk but do not yet show symptoms, which is particularly relevant given that Indian heart disease patients are often a decade younger than their Western counterparts.
However, the review cautions against premature adoption. The authors stress that AI tools must be rigorously evaluated in Indian settings, with local data, before they can be recommended for public health use. They also call for greater transparency from developers and for regulatory frameworks that ensure safety and equity.
For now, traditional risk assessment methods—based on blood pressure, cholesterol, smoking history, and other established factors—remain the standard of care. AI should be seen as a complement, not a replacement, to those methods. As research advances and the technology matures, these tools may eventually earn a place in the clinic. But that day has not yet arrived.
The findings serve as a reminder that innovation alone is not enough; it must be accompanied by careful validation, ethical safeguards, and a commitment to improving health outcomes for all, not just for those who are easy to measure.
