WORLD JOURNAL OF ADVANCE
HEALTHCARE RESEARCH

Dr. Ajay Kumar Lal*

ABSTRACT

With its potential for both therapeutic and diagnostic uses, artificial intelligence (AI) has emerged as one of the most important and talked-about topics in medical imaging research. AI approaches are being used by researchers more and more to produce quantitative assessments of radiographic features and to spot intricate patterns in imaging data. AI support several treatment phases in radiation oncology, such as tumor delineation and post-therapy evaluation. Massive amounts of imaging data are processed by AI, which makes it possible to identify illness characteristics that are frequently invisible to human observers. Traditionally, human skill has been used to interpret radiological pictures, which are crucial for medical diagnosis and treatment planning. However, because of the complexity of images, errors and unpredictability may occur. Artificial intelligence now provides potent answers of this issue by rectifying prominent image generation followed by their approximate interpretation. Notwithstanding these advancements, a number of obstacles must be overcome before broad clinical integration can take place. These include the requirement for standardized data standards, privacy constraints, ethical and policy considerations surrounding data sharing, and interpretability restrictions in models. The goal of this scoping study is to thoroughly assess how artificial intelligence (AI) can improve the precision and effectiveness of diagnostic imaging with its future challenges and limitations to be overcome.