Convergence of medical big data and artificial intelligence: a new paradigm for disease prediction

Abstract

Objective This study aims to systematically explore the interdisciplinary innovative application of deep integration of medical big data and artificial intelligence (AI) technology in the field of disease prediction, and promote the accurate and dynamic innovation of disease prediction and diagnosis paradigm by integrating multimodal medical data and intelligent algorithms. Methods A multi-center, multi-source heterogeneous data collection strategy was used to collect clinical electronic medical records, high-resolution medical imaging and whole genome sequencing data. Data standardization and differential privacy anonymization technology were implemented based on HIPAA standard to ensure data security and compliance. The research relies on deep convolutional neural network (CNN) to realize the fine-grained feature extraction and lesion recognition of medical images. Ensemble learning and graph mining algorithm were combined to mine potential disease risk factors from time-series clinical data. To analyze the genotype-phenotype association by genome-wide association study (GWAS) and Bayesian network model, and to construct an individualized disease risk prediction framework. To develop a clinical decision support system (CDSS) through multimodal data fusion and real-time analysis engine to provide dynamic, evidence-based diagnosis and treatment recommendations for doctors. Results AI technology significantly improved the accuracy in the early screening and diagnosis of skin cancer, Alzheimer's disease and other diseases. Personalized medicine based on the patient's genetic information improves the treatment effect and the quality of life of patients. The integration of medical big data and AI has accelerated the process of new drug research and development and optimized the design of clinical trials. Conclusion The deep integration of medical big data and AI realizes the paradigm transition from population generalization to individual precision in disease prediction through algorithm innovation and cross-modal data collaboration. In the future, it is necessary to further break through the bottlenecks such as model interpretability, multi-center data interoperability and ethical compliance, so as to promote the large-scale application of this paradigm in the intelligent medical ecology and provide the core driving force for precision medicine and public health decision-making.