AI is transforming cardiology, improving the accuracy of diagnosis, the prediction of outcomes and treatment planning. However, its use also raises ethical concerns, including bias, privacy, accountability and the risk of making healthcare less human. This narrative review explores these challenges, focusing on bias in training data, data privacy, the “black-box” nature of many AI systems, and the need for transparency and accountability in clinical decisions.
The authors explain that if the data used to train an AI model does not adequately represent minority groups or people with lower socioeconomic status, the model may be less accurate for them. This is particularly concerning in cardiology, because conditions such as hypertension, diabetes and heart disease disproportionately affect marginalised populations. They describe examples such as a cardiovascular risk tool that favoured White patients because its training data was not diverse enough, and AI-powered wearable devices that people on lower incomes could not access.
To address these risks, the authors call for diverse datasets that represent different ethnicities, genders and socioeconomic backgrounds, testing of AI tools across demographic groups, continuous monitoring once they are in use, and close collaboration between clinicians and AI. They conclude that the success of AI in cardiology depends on strong frameworks that promote fairness, transparency and privacy, and note that real-world validation is still lacking.
Learn more about this review here: https://doi.org/10.1097/MS9.0000000000003349
Reference:
Patel, D., Chetarajupalli, C., Khan, S., Khan, S., Patel, T., Joshua, S., & Millis, R.M. (2025). A Narrative Review on Ethical Considerations and Challenges in AI-Driven Cardiology. Annals of Medicine & Surgery, 87, 4152–4164.