Artificial intelligence in echocardiography: trends, hotspots, and future directions
From 2019 to 2024, annual publications increased rapidly, reflecting accelerated advancements in AI-driven echocardiography. Key research hotspots encompass automated image analysis, diagnostic classification, prognostic prediction, and workflow optimization, with deep learning models demonstrating significant potential to reduce operator dependency and enhance diagnostic precision. Future research will prioritize three critical challenges: data standardization across heterogeneous sources, development of fully automated diagnostic pipelines, and improving the clinical reliability of AI systems.
Authors
- Xiaohua Pei (ORCID: https://orcid.org/0000-0003-3043-8874)
- Yanli Sun
- Hongzhe Zhang (ORCID: https://orcid.org/0000-0003-3850-8611)
- Qi Guo (ORCID: https://orcid.org/0000-0003-0439-8274)
- Jiafu Ma
- Yuting Lv (ORCID: https://orcid.org/0000-0002-0076-1913)
- Tiantian Yang (ORCID: https://orcid.org/0000-0002-0148-396X)
Institutions
- Fujian University of Traditional Chinese Medicine (CN)
- Shandong University of Traditional Chinese Medicine (CN)
- Beijing University of Chinese Medicine (CN)
- People's Liberation Army 401 Hospital (CN)
- Shandong Provincial Hospital (CN)
- Jinan City People's Hospital (CN)
- Shandong First Medical University (CN)
Publication Details
- Journal
- International Journal of Surgery
- Published
- 2025-11-13
- DOI
- https://doi.org/10.1097/js9.0000000000004061
- Primary Topic
- Cardiovascular Function and Risk Factors
- Type
- article
- Field-Weighted Citation Impact
- 0.00