Explainability of multi-modal machine learning and deep learning applications in health

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.