Artificial Intelligence in Multi-Disease Medical Diagnostics: An Integrative Approach

Abstract

With advanced algorithms, artificial intelligence (AI) has revolutionized the medical diagnostic field where diseases can be predicted simultaneously. The integrative nature of this approach is novel because it can better encompass the complexity of comorbid conditions that are so common in patients; thus, addressing them in a more holistic diagnostic tone that is lacking in previous works. In this study, the investigation of the usage of AI models for simultaneously diagnosing diseases like diabetes, cardiovascular conditions, and neurological disorders is done. Therefore, based on AI techniques i.e. artificial neural networks (ANNs) and ensemble learning methods, a multi-disease diagnostic framework was developed to achieve this. A variety of features, related to each condition, were captured from multi-modal datasets including imaging, laboratory test results, and patient histories. The system was developed to manage the big flow of aggregated data and offer detailed diagnostic views of many diseases. Sensitivity, specificity, and overall diagnostic accuracy were used to evaluate the framework's performance. The results showed that the AI framework has high diagnostic accuracy for all targeted conditions an overall sensitivity of 93% and a specificity of 91%. Importantly, the combination of multi-modal data proved to substantially improve the system’s ability to identify and distinguish comorbid conditions. It makes the importance of using various data sources to benefit from the reliability and comprehensiveness of AI diagnostics obvious. Overall, AI-driven multi-disease diagnostic systems provide great promise for the role of delivering potentially transformative clinical healthcare workflow improvements, reducing errors, and improving patient outcomes. These frameworks will need to be scaled and tested in various healthcare settings and also across more varied diseases to help make medical diagnosis more available and effective.