An introduction to Self-Aware Deep Learning for medical imaging and diagnosis

Abstract

Aim: This study represents preliminary research for testing the effectiveness of the Self-Aware Deep Learning (SAL) methodology in the context of medical diagnostics using various types of attributes. By enhancing traditional AI models with self-aware capabilities, this approach seeks to improve diagnostic accuracy and patient outcomes in medical settings. Methods: This research discusses an introduction of SAL methodology into the medical field. SAL incorporates continuous self-assessment, allowing the AI to adjust its parameters and structure autonomously in response to changing inputs and performance metrics. The methodology is applied to medical diagnostics, utilizing real medical datasets available in the public domain. These datasets encompass a partial range of medical conditions and diagnostic scenarios, providing an initial test background for a preliminary evaluation of the effectiveness of SAL in a real-world context. Results: The study shows encouraging results in enhancing diagnostic accuracy and patient outcomes. Through continuous assessment and autonomous adjustments of its own neural network architecture, self-aware AI systems show improvements in adaptability, in the classification of real datasets and diagnostic process. Additional experiments on expanded data sets are necessary for validating these preliminary results. Conclusions: Tests on real data show that Self-Aware Deep Neural Networks present promising potential for improving medical diagnostic capabilities. They offer advantages such as enhanced adaptability to varying data qualities, improved error detection, efficient resource allocation, and increased transparency in AI-assisted diagnoses. However, considering the limited size of the test data set used in this research, further validation is required through additional experiments on larger data sets.