Summary of Artificial Intelligence in Pediatric Echocardiography: Exploring Challenges, Opportunities, and Clinical Applications with Explainable Ai and Federated Learning, by Mohammed Yaseen Jabarulla et al.
Artificial Intelligence in Pediatric Echocardiography: Exploring Challenges, Opportunities, and Clinical Applications with Explainable AI and Federated Learning
by Mohammed Yaseen Jabarulla, Theodor Uden, Thomas Jack, Philipp Beerbaum, Steffen Oeltze-Jafra
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the potential of artificial intelligence (AI) in pediatric echocardiography analysis. AI can facilitate automated interpretation of data, but there are challenges such as limited public data availability, data privacy, and AI model transparency. The study reviews the limitations and opportunities of AI in pediatric echocardiography, emphasizing the role of explainable AI (XAI) and federated learning (FL) in improving diagnostic and decision support workflows. The authors also identify research gaps and potential future developments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI can help doctors with pediatric heart diseases by analyzing echo images more accurately. Currently, doctors rely on echocardiography as a central imaging method to diagnose these conditions. However, AI can make this process faster and more accurate. The study discusses the challenges of using AI in this field, such as limited data availability and model transparency. |
Keywords
» Artificial intelligence » Federated learning
