Leveraging Artificial Intelligence for Early Disease Detection and Prediction: A Multi-Modal and Explainable Approach to Precision Healthcare

Abstract

Abstract: This dissertation looks into how artificial intelligence (AI) can help find and predict diseases early in precision healthcare. The main research issue is about using different types of data, like medical images, genetic information, and clinical records, while also focusing on the need for AI models that can explain their decisions. The results show that using various datasets, which include patient histories, demographic info, and health results, new AI methods can significantly improve the accuracy of diagnoses and insights about disease outcomes. Notably, the AI models used in this study are better than traditional diagnostic methods and offer outputs that doctors can easily understand and rely on. This research highlights the importance of AI in enabling quick treatments and tailored care plans, which can lead to better patient outcomes and more efficient healthcare. The wider implications of this study include building a stronger system for data-driven choices in healthcare, encouraging the use of explainable AI in medical practices, and setting the stage for new developments in precision medicine. This work is an important step towards managing the challenges of using advanced technology in healthcare, ultimately improving the quality and availability of medical services. This research aims to examine how artificial intelligence can be used for early disease detection and prediction in precision healthcare systems. The main issue is how to combine various data sources, like medical images, genomic data, and clinical records, while ensuring that AI models are understandable. This requires gathering different datasets, including patient histories, demographic details, and health results, to confirm the suggested methods.