AI Detects Potential Crises in Emergency Medicine in Seconds: Applying Multi-Data Integration and Privacy Protection Technology

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Kaohsiung Medical University's Institute of Artificial Intelligence and Biomedicine has utilized AI to reduce the time required for image diagnosis from 5-10 minutes (by human doctors) to less than one second. This enables rapid response in emergency settings. The system does not merely analyze X-rays but integrates diverse data types, including medical images, pathology slides, genetic data, and lab reports. This allows for early prediction of potential risks like cancer or acute kidney injury. To alleviate the burden on medical staff, a dedicated internal generative AI platform was built. This uses open-source Large Language Models (LLMs) combined with Retrieval-Augmented Generation (RAG) technology to assist in drafting nursing records and medical summaries. However, challenges such as 'patient privacy' and 'interoperability limitations between hospitals' hinder nationwide adoption. To address this, the university adopted 'Federated Learning.' This technique ensures that raw patient data never leaves the hospital; only calculated parameters or experience are shared with the AI, thus maintaining privacy while improving accuracy. Furthermore, advanced AI requires massive computing power, necessitating infrastructure like water-cooled data centers. To prevent AI bias, the training data includes cross-area population samples from Southern Taiwan to ensure fairness and reduce disparities based on region or resources.