An Integrated Hybrid Artificial Intelligence Approach For Multilingual Clinical Text Understanding And Adaptive Drug Delivery Optimisation

Abstract

The speedy increase in medical data, particularly, unstructured multilingual clinical text, poses a considerable challenge to the correct interpretation and effective decision-making in drug delivery systems. The paper suggests a hybrid, integrated artificial intelligence (ai) system, which incorporates natural language processing (nlp), machine learning (ml), and deep learning (dl) to facilitate multilingual clinical text interpretation and maximize adaptive drug delivery. The system uses language models based on transformers to extract semantics, models based on hybrid classification to predict diagnoses, and models based on reinforcement learning to optimize individual dosage of drugs. The experimental findings show that there is better accuracy, less response time as well as increased personalization than the traditional systems. In practice, however, there are also such limitations as reliance on large multilingual annotated datasets, computational complexity, and difficulties in dealing with rare inter-language medical terms. More future work directions include federated learning to preserve privacy, support low-resource languages, and incorporate real-time iot-based physiological data to increase versatility in clinical settings.

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