Artificial intelligence in medical imaging diagnosis: are we ready for its clinical implementation?

Abstract

Purpose: We examine the transformative potential of artificial intelligence (AI) in medical imaging diagnosis, focusing on improving diagnostic accuracy and efficiency through advanced algorithms. It addresses the significant challenges preventing immediate clinical adoption of AI, specifically from technical, ethical, and legal perspectives. The aim is to highlight the current state of AI in medical imaging and outline the necessary steps to ensure safe, effective, and ethically sound clinical implementation.

Approach: We conduct a comprehensive discussion, with special emphasis on the technical requirements for robust AI models, the ethical frameworks needed for responsible deployment, and the legal implications, including data privacy and regulatory compliance. Explainable artificial intelligence (XAI) is examined as a means to increase transparency and build trust among healthcare professionals and patients.

Results: The analysis reveals key challenges to AI integration in clinical settings, including the need for extensive high-quality datasets, model reliability, advanced infrastructure, and compliance with regulatory standards. The lack of explainability in AI outputs remains a barrier, with XAI identified as crucial for meeting transparency standards and enhancing trust among end users.

Conclusions: Overcoming these barriers requires a collaborative, multidisciplinary approach to integrate AI into clinical practice responsibly. Addressing technical, ethical, and legal issues will support a softer transition, fostering a more accurate, efficient, and patient-centered healthcare system where AI augments traditional medical practices.