Artificial intelligence (AI) is transforming many fields, including cardiology and cardiovascular surgery, where it offers improvements in diagnostics, treatment plans, and predictive analytics.[1] However, its integration into healthcare presents challenges that could negatively affect patient outcomes, physician roles, and the healthcare system. In this article, we explored these risks in greater depth, considering the implications for cardiologists and cardiovascular surgeons, as well as patients.

Diagnostic errors and limitations
One significant risk of AI in cardiovascular care is the potential for diagnostic errors.[1] These systems rely heavily on the quality of data they are trained on, and if the data is incomplete, biased, or of low quality, the results can be inaccurate. This may lead to AI misinterpreting subtle cardiac abnormalities or missing rare conditions that a skilled physician might detect. The current inability of AI to fully integrate a patient"s clinical factors, such as history and lifestyle, limits its effectiveness, as cardiology requires more than just data points; it demands a holistic understanding of each patient.

Erosion of physician expertise
Artificial intelligence"s growing role in tasks such as echocardiogram analysis could lead to an overreliance on technology, risking the loss of essential clinical skills in cardiologists. While AI operates based on patterns and probabilities, it lacks the clinical judgment and insight gained from years of experience that human doctors use in complex decision-making.[2] Overreliance on AI could result in "deskilling" physicians, reducing their ability to make independent clinical judgments and impacting patient trust. Artificial intelligence should assist, not replace, human expertise, particularly when personalized treatment is required.

Data security and privacy concerns
The use of AI in cardiology requires vast amounts of sensitive data, such as patient history, imaging, and genetic information. This data, if not properly secured, could be vulnerable to cyberattacks, unauthorized access, or breaches, raising significant privacy concerns. The sharing of patient data across platforms, including with third-party tech companies, brings ethical questions about patient consent and the commercialization of health data. Additionally, AI"s "black box" nature, where decision-making processes are not transparent, can make it difficult for physicians to explain AI-driven outcomes to patients, potentially eroding trust in the system.[3]

Over-dependence on AI
Though AI offers exceptional speed and accuracy, overdependence could hinder the development of essential clinical skills. Physicians who rely too much on AI risk losing their ability to make critical, on-the-spot decisions, particularly in emergencies where AI may not be readily available or capable of handling unpredictable scenarios.[1] In such cases, delays in human intervention could result in harmful outcomes. It is crucial that AI remains a supportive tool and does not replace clinical expertise.

Inadequate training and misuse of AI
The rapid adoption of AI has outpaced physician training, leading to the risk of improper use or misinterpretation of AI outputs. Understanding AI"s limitations is just as important as recognizing its strengths. Without adequate training, healthcare providers may place too much trust in AI-generated results, increasing the potential for errors in patient care.[4] Healthcare systems must prioritize comprehensive AI training to ensure that physicians can critically evaluate AI outputs and maintain high standards of patient safety.

Algorithmic bias and healthcare inequality
Artificial intelligence systems are often trained on historical data, which may contain biases that impact their performance across different demographic groups. Cardiovascular disease affects diverse populations in distinct ways, and if AI models are primarily trained on data from one demographic, such as Caucasian males, they may provide less accurate results for female patients, minorities, or underserved groups.[5] To avoid perpetuating healthcare inequalities, AI models must be trained on diverse datasets that reflect the unique characteristics and needs of various populations. Ensuring fairness and inclusivity in AI development is critical to reducing healthcare disparities.

Impact on the doctor-patient relationship
Artificial intelligence"s ability to generate rapid treatment recommendations could reduce the amount of time physicians spend communicating with patients, potentially leading to a more impersonal healthcare experience. In cardiovascular care, particularly for patients with chronic conditions, regular communication and emotional support from a physician are vital. The efficiency of AI might inadvertently reduce these human interactions, leaving patients feeling disconnected or undervalued. Maintaining the human element of care is essential for fostering trust and ensuring patient satisfaction.

Legal and ethical liability
The integration of AI into cardiovascular practice raises legal and ethical questions about responsibility when errors occur. If AI makes an incorrect diagnosis or treatment suggestion, determining whether the physician or the AI developer is at fault is complex.[1] Clear guidelines are needed to define liability and establish standards of care when AI is involved. Cardiovascular care giver physicians may find themselves in a difficult position, having to balance AI recommendations with their own clinical judgment, which could expose them to legal risks if they either follow or ignore AI guidance.

In conclusion, AI has the potential to revolutionize cardiovascular care by improving diagnosis and treatment. However, it is not without risks, including diagnostic errors, privacy concerns, overreliance on technology, and erosion of clinical skills. To maximize its benefits and minimize risks, healthcare providers must approach AI cautiously, using it as a complement to human expertise rather than a replacement. Comprehensive training, ethical safeguards, and diverse, inclusive datasets are essential to ensure that AI improves cardiovascular care for all patients without exacerbating existing inequalities or undermining trust in healthcare.

Data Sharing Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.

Author Contributions: The conception of the work; or the acquisition, analysis, or interpretation of data for the work, drafting the work, reviewing it critically for important intellectual content, final approval of the version to be published: H.G., A.B.D.

Conflict of Interest: The authors declared no conflicts of interest with respect to the authorship and/or publication of this article.

Funding: The authors received no financial support for the research and/or authorship of this article.

References

1) Khera R, Oikonomou EK, Nadkarni GN, Morley JR, Wiens J, Butte AJ, et al. Transforming cardiovascular care with artificial intelligence: From discovery to practice: JACC state-of-the-art review. J Am Coll Cardiol 2024;84:97-114. doi: 10.1016/j.jacc.2024.05.003.

2) Torres-Soto J, Ashley EA. Multi-task deep learning for cardiac rhythm detection in wearable devices. NPJ Digit Med 2020;3:116. doi: 10.1038/s41746-020-00320-4.

3) Cohen IG, Amarasingham R, Shah A, Xie B, Lo B. The legal and ethical concerns that arise from using complex predictive analytics in health care. Health Aff (Millwood) 2014;33:1139-47. doi: 10.1377/hlthaff.2014.0048.

4) Youssef A, Pencina M, Thakur A, Zhu T, Clifton D, Shah NH. External validation of AI models in health should be replaced with recurring local validation. Nat Med 2023;29:2686-7. doi: 10.1038/s41591-023-02540-z.

5) Duffy G, Clarke SL, Christensen M, He B, Yuan N, Cheng S, et al. Confounders mediate AI prediction of demographics in medical imaging. NPJ Digit Med 2022;5:188. doi: 10.1038/ s41746-022-00720-8.