Intelligent Integration of Generative AI in Medical Diagnostics and Data Analysis for Next-Generation Healthcare Systems

Healthcare systems around the world are working to address the unmet medical requirements of people with a variety of acute and chronic illnesses. One of the main objectives is to guarantee that every citizen has access to high-quality, reasonably priced healthcare, and governments are increasingly assessing the effectiveness of healthcare treatments, including what constitutes a cost-effective therapy. Medical diagnostics and data analysis are becoming crucial for next-generation healthcare systems, even if previous discussions have mostly ignored the significance of information, especially the VODI. By enabling more effective and efficient treatment while keeping costs under control, investing in diagnostic data has the potential to drastically change the way healthcare is delivered. With an emphasis on the detection of thyroid disease patients, our work suggests a technique that uses PCA and normalization to prepare medical diagnostic data and extract useful features for analysis. Using the LMA for backpropagation, the approach combines PCA with a hybrid model that combines a GA and a classical BPNN to improve prediction accuracy. The suggested GABPAN model outperforms current models with a success rate of 95.40 %. These results highlight how crucial diagnostic data is to enabling accurate, data-driven decision-making in next-generation healthcare systems.