Year: 2025
Type: dissertation
Abstract: Artificial Intelligence (AI) continues to be developed and has the potential to be incorporated into health, allowing for more efficient and effective diagnosis.However, many AI models operate as "black-boxes" in how conclusions are drawn or made, leading to a lack of trust.Explainable AI (XAI) has the potential to enable clinicians and patients to understand why a model made the prediction that it made -either for model debugging or for deriving clinically useful insights.This study proposes a pose-inspired framework for autism-based video behavioral analysis, exploring feature influence scores for a Long-Term Short-Term Memory+Neural Network (LSTM+NN) applied to video data.The potential for applicability of multimodal XAI is further shown on publicly available tabular data using variations of a Random Forest model for diabetes diagnosis.
Author: Nitta, Amanda
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7110513885