Niyogi SG, Nag DS, Shah MM, Swain A, Naskar C, Srivastava P, Kant R. Role of artificial intelligence in congenital heart disease. World J Clin Pediatr 2025; 14(3): 105926 [DOI: 10.5409/wjcp.v14.i3.105926]
Corresponding Author of This Article
Deb Sanjay Nag, Department of Anaesthesiology, Tata Main Hospital, C Road West, Northern Town, Bistupur, Jamshedpur 831001, Jharkhand, India. ds.nag@tatasteel.com
Research Domain of This Article
Pediatrics
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Clin Pediatr. Sep 9, 2025; 14(3): 105926 Published online Sep 9, 2025. doi: 10.5409/wjcp.v14.i3.105926
Role of artificial intelligence in congenital heart disease
Subhrashis Guha Niyogi, Deb Sanjay Nag, Mandar Mahavir Shah, Amlan Swain, Chandrima Naskar, Preeti Srivastava, Ravi Kant
Subhrashis Guha Niyogi, Deb Sanjay Nag, Amlan Swain, Ravi Kant, Department of Anaesthesiology, Tata Main Hospital, Jamshedpur 831001, Jharkhand, India
Mandar Mahavir Shah, Department of Cardiology, Tata Main Hospital, Jamshedpur 831001, Jharkhand, India
Chandrima Naskar, Department of Psychiatry, Tata Main Hospital, Jamshedpur 831001, Jharkhand, India
Preeti Srivastava, Department of Paediatrics, Tata Main Hospital, Jamshedpur 831001, Jharkhand, India
Author contributions: Niyogi SG, Nag DS, and Shah MM designed the overall concept and outline of the manuscript; Swain A, Naskar C, Srivastava P, and Kant R contributed to the discussion and design of the manuscript; Niyogi SG, Nag DS, Shah MM, Swain A, Naskar C, Srivastava P, and Kant R contributed to the writing and editing of the manuscript and review of literature.
Conflict-of-interest statement: There is no conflict of interest associated with any of the senior author or other coauthors contributed their efforts in this manuscript.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Deb Sanjay Nag, Department of Anaesthesiology, Tata Main Hospital, C Road West, Northern Town, Bistupur, Jamshedpur 831001, Jharkhand, India. ds.nag@tatasteel.com
Received: February 11, 2025 Revised: April 8, 2025 Accepted: May 7, 2025 Published online: September 9, 2025 Processing time: 126 Days and 3.4 Hours
Core Tip
Core Tip: Artificial intelligence (AI) offers transformative potential for congenital heart disease (CHD) care, affecting diagnosis, management, and long-term monitoring. This study explores the multifaceted applications of AI across the journey of patients with CHD, highlighting key advancements and critical challenges. Prenatally, AI-enhanced fetal echocardiography and genetic testing promise earlier and more accurate diagnosis, allowing for timely intervention. Postnatally, AI-driven image analysis accelerates diagnosis, and advanced signal processing improves hemodynamic assessment. AI-driven decision support systems tailor treatment strategies based on individual patient characteristics. Long-term care benefits from AI-enabled remote monitoring and wearable technologies, facilitating proactive management and early detection of complications. However, realizing the full potential of AI in CHD requires addressing significant limitations. The development of robust, standardized datasets is crucial for training reliable AI models. Furthermore, ensuring the transparency and explainability of AI algorithms is essential for building trust and accountability. Ethical considerations, including data privacy, bias mitigation, and equitable access, must be carefully addressed. Finally, the seamless integration of AI tools into existing clinical workflows is vital for practical implementation and widespread acceptance. Addressing these challenges will pave the way for AI to revolutionize CHD care, achieve better outcomes, and improve the lives of patients and their families.
