ADVANCEMENTS IN AI-BASED INTERPRETATION OF MEDICAL IMAGES: COMPREHENSIVE REVIEW

Articial Intelligence (AI) applications in radiology are rapidly evolving, transforming medical imaging

interpretation and diagnosis. This review explores emerging trends in AI applications for radiology,

highlighting key developments that are reshaping clinical practice and patient care. One major trend is the development of AI

models for multi-modal imaging analysis, enabling simultaneous assessment of different imaging modalities for improved

diagnostic accuracy and treatment planning. Additionally, AI integration into radiology workow management systems is

streamlining interpretation processes, prioritizing urgent cases, and optimizing resource allocation. AI is also enhancing

image quality and reconstruction, enabling radiologists to visualize subtle details more effectively. Decision support tools

powered by AI are assisting radiologists in interpreting images, providing differential diagnoses, and recommending follow-up

actions. Furthermore, AI is advancing personalized medicine in radiology by analyzing imaging data alongside genetic,

clinical, and demographic information to predict disease progression and treatment response. The democratization of AI in

radiology is enabling radiologists of varying expertise to utilize AI algorithms for enhanced diagnostic capabilities. These

trends promise to enhance diagnostic accuracy, improve workow efciency, and enable personalized patient care. As AI

continues to advance, its integration into radiology practice is expected to have a transformative impact.