Summary of Evaluating the Explainable Ai Method Grad-cam For Breath Classification on Newborn Time Series Data, by Camelia Oprea et al.
Evaluating the Explainable AI Method Grad-CAM for Breath Classification on Newborn Time Series Data
by Camelia Oprea, Mike Grüne, Mateusz Buglowski, Lena Olivier, Thorsten Orlikowsky, Stefan Kowalewski, Mark Schoberer, André Stollenwerk
First submitted to arxiv on: 13 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY); Machine Learning (cs.LG)
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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 A machine learning model that can classify breath patterns in neonatal ventilation data using a neural network is proposed. The explanation method Grad-CAM is used to provide insight into the decision-making process, but its usefulness remains unclear. A user study based evaluation of Grad-CAM is conducted to assess its perceived usefulness among different stakeholders. The results show that many participants desire more in-depth explanations, highlighting the difficulty in achieving actual transparency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to use artificial intelligence in medicine is being tested. A special kind of machine learning called explainable AI tries to help people understand how AI makes decisions. But it’s not clear if this helps or hurts. This study looks at a specific method called Grad-CAM and sees how doctors, nurses, and others like the explanations they get from it. Most people want more detailed explanations, which shows that making AI transparent is still a challenge. |
Keywords
» Artificial intelligence » Machine learning » Neural network
