Design Considerations for High Impact, Automated Echocardiogram Analysis
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Abstract (arXiv)
Deep learning has the potential to automate echocardiogram analysis for early detection of heart disease. Based on a qualitative analysis of design concerns, this study suggests that predicting normal heart function instead of disease accounts for data quality bias and significantly increases efficiency in cardiologists' workflows.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Jun 2020.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 2.
4 papers.
- 2023Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarizationcited by 244
- 2022Tiny, always-on and fragile: Bias propagation through design choices in on-device machine learning workflowscited by 8
- 2025MedVAL: Toward Expert-Level Medical Text Validation with Language Modelscited by 3
- 2025EchoQA: A Large Collection of Instruction Tuning Data for Echocardiogram Reportscited by 1
- 2025MedVAL: Toward Expert-Level Medical Text Validation with Language Models3 Jul 2025
- 2025EchoQA: A Large Collection of Instruction Tuning Data for Echocardiogram Reports4 Mar 2025
- 2023Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization14 Sep 2023
- 2022Tiny, always-on and fragile: Bias propagation through design choices in on-device machine learning workflows19 Jan 2022