Optimizing Diagnostic Accuracy in Healthcare by Using Deep Learning

Abstract

The increasing demand for efficient, accurate, and accessible healthcare solutions has driven advancements in AI-driven diagnostics. This research presents an AI-powered medical diagnostic system that integrates deep learning and NLP to further enhance disease detection, medical report analysis, and drug identification. The proposed system integrated Gemini H1 for live AI-driven insight generation over medical images and text data for precise diagnosis and treatment recommendations. The system has a multi-modal data processing capability that allows inputting images and text for detailed analysis. Additionally, it provides a medication management feature that imparts information regarding drug usage instructions, side effects, and any possible drug interactions. The back-end architecture is built in Django and Django Rest Framework, so it has secured authentication and pacific API management. Also, the front end is built using React Native, providing support for both patients and healthcare professionals. Medical image processing and NLPbased report analysis AI models were built using TensorFlow, PyTorch, OpenCV, and spaCy, while deployment is on the cloud for scalability. Furthermore, an AI-powered health search engine is integrated into this system, allowing the quick and accessible search for the disease and treatment information to improve medical access, especially in remote areas. This sharpening of an AI-driven solution highlights the challenges of manual diagnoses that are time-consuming, human errors, limited access to healthcare specialists, and high costs of diagnosis. Hence, such an AI-based solution enhances diagnostic accuracy, decreases the overall processing time, and provides cost-effective healthcare service solutions. In this regard, the proposed system is unique in real-time AI diagnostics, multi-modal data integration, and explainable AI insights geared toward a readily understandable, practical, and reliable tool for modern healthcare applications.

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