Advancements In Artificial Intelligence And Machine Learning For Early Cardiovascular Risk Prediction And Diagnosis - Kartika Ririe, Avrina; Khan, Mahnoor; Abbas Bangash, Sudhair; Mahmoud Younes Elsherbiny, Reem Maged; Umer Ali Ayub, Muhammad; Louis, Ernst; Akter Mitu, Momtaz; Hasan Pritom, Mehedi; Edo Tondas, Alexander (Journal of Neonatal Surgery, 2025)
Background: Cardiovascular diseases (CVDs) continue to be a leading cause of death globally, with early detection being crucial in reducing morbidity and mortality. Artificial Intelligence (AI) and Machine Learning (ML) technologies have emerged as powerful tools for predicting cardiovascular risk and enhancing early diagnosis. Despite the growing interest in AI/ML applications in healthcare, the extent to which these technologies impact cardiovascular health prediction and diagnosis remains underexplored. Objective: The primary objective of this study is to evaluate the effectiveness of AI and ML in predicting cardiovascular risk and enabling early diagnosis. The research seeks to assess how AI/ML can contribute to improving diagnostic accuracy, identifying high-risk patients, and facilitating personalized treatment options in the context of cardiovascular health. Methods: An online survey was administered to a diverse group of 160 respondents, including healthcare professionals, data scientists, and the general public. The questionnaire was designed to capture participants' awareness of AI/ML in healthcare, their confidence in AI's ability to predict cardiovascular risks, and their perceptions of the benefits and challenges associated with AI/ML technologies in healthcare. The survey included both quantitative (Likert-scale) and qualitative (open-ended) questions. Quantitative data were analyzed using descriptive statistics, and qualitative data were analyzed through thematic analysis to identify key themes and insights. Results: The findings indicate that a majority of respondents (80%) believe AI/ML to be effective in predicting cardiovascular risks, with most participants acknowledging its potential to improve diagnostic accuracy and patient outcomes. However, several challenges, including data privacy concerns (50%) and lack of skilled professionals (45%), were highlighted as barriers to widespread AI adoption. Additionally, 65% of respondents expressed high trust in AI for healthcare decision-making, although many emphasized the need for human oversight. The study also found that AI/ML has significant potential in reducing cardiovascular mortality and improving the overall quality of care through early diagnosis and personalized treatment plans. Conclusion: This study underscores the promising role of AI and ML in predicting cardiovascular risks and early diagnosis. While most participants recognized the benefits of these technologies, barriers such as data privacy, cost, and the need for skilled professionals remain challenges to their implementation. Future research should focus on overcoming these barriers and developing more [1]healthcare professionals to effectively integrate AI/ML tools into clinical practice.