AI-DRIVEN METHODOLOGICAL FRAMEWORKS FOR IMAGE AND SIGNAL PROCESSING IN BIOMEDICAL ENGINEERING

Abstract

Novelty Statement: This paper gives a quantitative review of AI-based frameworks, more specifically how the improvement factors including Accuracy rates, False positive rates and processing time impact the optimization of AI models in biomedical applications.

Material and Methods: The research conducted in this study is quantitative, and data is collected from the AI models applied in biomedical diagnostics such as convolution neural networks (CNNs) and recurrent neural networks (RNNs). Data were gathered from a simulated environment, questionnaire reports, and live clinical usage. Shapiro-Wilk tests were used to test the normality of the data that was followed by regression analysis and Cronbach’s alpha to test the internal consistency reliability of the KPIs.

Results and Discussion: Some of the findings revealed that AI models have very high diagnostic accuracy with most systems at an average of 85%. However, it is also conspicuously clear that the false positive rates and cost efficiency factors do vary, which calls for further model optimization. The assumption of normality was checked and validated from the statistical analysis and the Cronbach alpha value indicates that the KPIs represent different dimensions of AI performance. This showed that there was no direct correlation between plotting accuracy, false positives, and time taken for processing hence the need for multi-dimensionality.

Conclusion: The application of algorithms in the diagnostics of biomedical-related disorders presents enormous prospects. However, it needs further fine-tuning to reduce false positives more and thus improve its cost-effectiveness. Based on the results of the study, it is crucial for adopting AI systems to approach investment in a balanced fashion to realize effectiveness and sustainability in a clinical context at the same time.