Artificial intelligence in cardiology: an updated systematic review with ethical considerations and challenges in implementing artificial intelligence models

The integration of artificial intelligence (AI) in cardiovascular medicine presents a transformative opportunity to enhance diagnostic accuracy and improve patient outcomes. This systematic review evaluates the impact of AI on cardiovascular diagnostics with a focus on its ability to surpass traditional methods in accuracy, efficiency, and predictive capabilities. Machine learning and deep learning have demonstrated significant advancements in areas such as echocardiography, electrocardiography, computed tomography angiography, and predictive analytics. AI algorithms have demonstrated superior performance in identifying subtle patterns and anomalies. Additionally, AI has shown promise in predictive analytics, forecasting disease progression and tailoring treatment plans, thereby improving patient outcomes. Despite these advancements, significant gaps remain in our understanding of AI's full impact on cardiovascular medicine. Challenges such as the generalizability of AI models, ethical considerations, data privacy, issues related to data quality, clear guidelines on AI implementation in clinical practice and potential biases in AI algorithms warrant further investigation. Key findings indicate that AI systems consistently achieve higher diagnostic accuracy, reduce inter-observer variability, and facilitate earlier detection of cardiovascular conditions, leading to improved patient outcomes. In conclusion, AI holds substantial promise for improving diagnostic accuracy and patient outcomes in cardiovascular medicine. This review provides valuable insights into the benefits and limitations of AI, guiding future research and clinical practice to ensure responsible and effective integration of AI technologies in cardiovascular health care.