Hybrid Transformer-Driven Healthcare: BERT—T5 Model for Precision Diagnosis and Adaptive Care Recommendations

Abstract

The categorization of sophisticated transformer architectures have written fresh doors for intelligent clinical decision support, specifically for disease analysis as well as personalized treatment plan. Although Bidirectional Encoder Representations from Transformers (BERT) provides classification of complex medical text with high precision and good contextual understanding, it does not provide the type of generative abilities required to devise a treatment recommendation specific to individual patients. On the other hand, the Text-to-Text Transfer Transformer (T5) is very good at natural language generation, but it is vulnerable to hallucinations, which limits its use in healthcare, where it is critical to know what the drug will do. To overcome these limitations, this paper proposes a novel approach Hybrid Transformer-Driven Healthcare, which is a unified BERT-T5 architecture that is dedicated to achieving the goal of offering precision diagnosis and adaptable care recommendations. The proposed hybrid system's T5 is used to generate logical and context-aware treatment recommendations and BERT to accurately classify symptoms and diseases. It also incorporates the use of a controlled form of decoding in order to reduce the number of hallucinations. The model is better than standalone transformer baselines in experimental evaluations on a multimodal clinical dataset with 94% accuracy in diagnostic classification and 92% accuracy in treatment generation with a hallucinations rate reduced to 3%. These reflect the capacity of transformer fusion to enhance reliability, constancy and clinical utility in computerized healthcare systems. The proposed framework is a major jump in AI-Assisted Clinical Decision Making, and offers scalable and robust architecture for the next-generation personalized health care applications.

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