# Testing of Deep Learning Model in Real World Clinical Setting: A Case Study in Obstetric Ultrasound

arXiv: https://arxiv.org/abs/2404.00032 · HTML: https://mlsanity.com/paper/2404.00032/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 22 Mar 2024.

## Builds on (most cited first, 2 of 2)

- [SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound](https://mlsanity.com/paper/1612.05601/) (2016)
- [Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes](https://mlsanity.com/paper/1904.09612/) (2019)

## Built on by (most cited first, 1 of 1)

- [Determining Fetal Orientations From Blind Sweep Ultrasound Video](https://mlsanity.com/paper/2504.06836/) (2025)

## Abstract (arXiv)

Despite the rapid development of AI models in medical image analysis, their validation in real-world clinical settings remains limited. To address this, we introduce a generic framework designed for deploying image-based AI models in such settings. Using this framework, we deployed a trained model for fetal ultrasound standard plane detection, and evaluated it in real-time sessions with both novice and expert users. Feedback from these sessions revealed that while the model offers potential benefits to medical practitioners, the need for navigational guidance was identified as a key area for improvement. These findings underscore the importance of early deployment of AI models in real-world settings, leading to insights that can guide the refinement of the model and system based on actual user feedback.