Artificial Intelligence in Medical Imaging: Current Applications, Limitations, and Future Perspectives - Narrative Review

Abstract

Artificial intelligence (AI) has developed from a theory to a technology that has become practical in current healthcare, especially in image diagnosis.This is a review of where current usage of this technology is, its limitations at present, and its possible futures.Our findings indicate that AI systems are already augmenting radiological practice across various imaging modalities, including X-ray, CT, and MRI.These tools show real promise in speeding up diagnosis by detecting subtle or early signs that might be missed during standard screenings, particularly lung and breast cancers, and by analyzing heart and brain images.However, many unanswered questions remain regarding the reliability of AI tools in routine clinical practice, despite their impressive technical performance.Model bias, a lack of varied, high-quality data, and difficult moral conundrums related to AI use are some of the main obstacles.We observe that clearer legal and regulatory frameworks, as well as greater transparency, are increasingly required, often referred to as "explainable AI".Looking ahead, the field is evolving.The next generation of AI may involve multimodal and foundation models that integrate imaging data with other clinical information.The use of AI is focused on supporting, not replacing, radiologists and on analyzing medical images.

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