Explainable AI for medical over-investigation identification
In the dynamic landscape of machine learning applications spanning diverse sectors, the pursuit of explainability has emerged as important to provide insights into these models deemed as “black boxes”. This paper delves into the relatively unexplored domain within healthcare, specifically targeting...
Saved in:
Main Author: | Suresh Kumar Rathika |
---|---|
Other Authors: | Fan Xiuyi |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/175038 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Demystifying AI: bridging the explainability gap in LLMs
by: Chan, Darren Inn Siew
Published: (2024) -
May I ask a follow-up question? Understanding the benefits of conversations inneural network explainability
by: Zhang, Tong, et al.
Published: (2024) -
TeLLMe what you see: using LLMs to explain neurons in vision models
by: Guertler, Leon
Published: (2024) -
Building more explainable artificial intelligence with argumentation
by: Zeng, Zhiwei, et al.
Published: (2020) -
Towards explainable artificial intelligence in the banking sector
by: Jew, Clarissa Bella
Published: (2024)