The Role of Artificial Intelligence in Improving Diagnostic Accuracy in Cardiology and Radiology: In-Depth Analysis of Ethical, Legal, and Clinical Implications across Multiple Medical Disciplines

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Ibrahim Hussain Alfenis, Saleh Hussain Nasser Al Fenais, Mezher Abdullah Alqarni, Meshari Qwailab Alharbi, Abdulrahim Essa Jassim Alramadhan, Khalid Saleh Abdullah Alsheyab, Mustafa Amin Salman Alhashim, Feras Fahad Mana Alasiri, Hussain Mohammed Ali Alnajrani, Maher Taher Abusanah, Bedour Obaid Albadrani.

Abstract

Background: The integration of Artificial Intelligence (AI) within cardiovascular imaging is transforming diagnostic processes across medical specialties, particularly in cardiology, radiology, and oncology. However, the complexities of AI models, notably those utilizing machine learning (ML) and deep learning (DL), raise significant ethical and legal concerns, particularly regarding their interpretability and decision-making transparency.


Methods: This review synthesizes existing literature on AI applications in cardiovascular imaging. It examines the methodologies employed, including supervised and unsupervised learning, deep learning frameworks such as convolutional neural networks (CNNs), and generative adversarial networks (GANs), and the implications for clinical practice. The analysis focuses on AI's ability to detect subtle patterns in imaging data, enhancing diagnostic accuracy and workflow efficiency.


Results: AI technologies have demonstrated remarkable capabilities in identifying cardiovascular abnormalities and improving imaging quality. Applications include real-time detection of coronary artery stenosis from CT angiography and predictive models for cardiovascular events. However, the opaque nature of AI decision processes complicates clinical acceptance, as healthcare professionals often lack insight into the rationale behind AI-generated outputs.


Conclusion: While AI holds promise for advancing diagnostic capabilities in cardiovascular care, the prevailing "black box" issue necessitates the development of explainable AI frameworks. Enhancing transparency and interpretability is crucial for fostering trust among clinicians and ensuring ethical implementation in clinical settings. Addressing these challenges is essential for the responsible integration of AI technologies into healthcare practices, ultimately improving patient outcomes.

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