# Design Considerations for High Impact, Automated Echocardiogram Analysis

arXiv: https://arxiv.org/abs/2006.06292 · HTML: https://mlsanity.com/paper/2006.06292/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2020.

## Built on by (most cited first, 4 of 4)

- [Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization](https://mlsanity.com/paper/2309.07430/) (2023)
- [Tiny, always-on and fragile: Bias propagation through design choices in on-device machine learning workflows](https://mlsanity.com/paper/2201.07677/) (2022)
- [MedVAL: Toward Expert-Level Medical Text Validation with Language Models](https://mlsanity.com/paper/2507.03152/) (2025)
- [EchoQA: A Large Collection of Instruction Tuning Data for Echocardiogram Reports](https://mlsanity.com/paper/2503.02365/) (2025)

## 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.