Artificial intelligence-driven transformative applications in disease diagnosis technology

The integration of artificial intelligence (AI) in medical diagnostics represents a transformative advancement in healthcare, with projected market growth reaching 188 billion by 2030. This comprehensive review examines the latest developments in AI-driven diagnostic technologies across multiple disease domains, particularly focusing on cancer, Alzheimer's disease (AD), and diabetes. Through systematic bibliometric analysis using GraphRAG methodology, we analyzed research publications from 2022 to 2024, revealing the distribution and impact of AI applications across various medical fields. In cancer diagnostics, AI systems have achieved breakthrough performances in analyzing medical imaging and molecular data, with notable advances in early detection capabilities across 19 different cancer types. For AD diagnosis, AI-powered tools have demonstrated up to 90 % accuracy in risk detection through non-invasive methods, including speech pattern analysis and blood-based biomarkers. In diabetes care, AI-integrated systems incorporating deep neural networks and electronic nose technology have shown remarkable accuracy in predicting disease onset before clinical manifestation. These developments collectively indicate a paradigm shift toward more precise, efficient, and accessible diagnostic approaches. However, challenges remain in standardization, data quality, and clinical implementation. This review synthesizes current progress while highlighting the potential for AI to revolutionize medical diagnostics through enhanced accuracy, early detection, and personalized patient care.