<i>Diagnosphere</i> : Diagnostic Intelligence and AI-Driven Global Network Optimizing Scans, Patient Health, Evaluation, and Research Ecosystem

Abstract

The contemporary medical system is severely stressed out by the enormous amount of medical imaging data and the need for more accurate patient monitoring. An architecture is introduced in this paper that uses AI for scan evaluation and tracking patient progress that allows real-time, collaborative, and secure AI analysis based on scans. The system utilizes embedding models, large language models (LLMs), and cloud computing to carry out tasks like anomaly detection, treatment evaluation, and recovery prediction [2] automatically. It guarantees the secure authentication of physicians and allows the collaboration initiated by the doctor, thus improving the accuracy and speed of clinical decisions. The suggested framework incorporates e-communication based on WebSocket for the quick sharing of knowledge among healthcare professionals, while better reporting tools, image data fusion, and preliminary image analysis are factors contributing to individualized and promptly provided medical evaluations. The system, developed using the Weaved application and AWS DynamoDB, provides very high data security, dependability, and scalability. In the end, such an AI-led approach is patient outcome-focused and it enhances the whole process of healthcare by utilizing the resources, managing the data, and executing the decisions fast and efficiently within an interlinked, intelligent, medical ecosystem [5].

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