AI support in MR imaging of incidental renal masses

Our study explores the integration of artificial intelligence (AI) into magnetic resonance (MR) imaging to enhance the differentiation between benign and malignant renal lesions. The findings suggest that AI can significantly improve diagnostic accuracy and cost-effectiveness, addressing a crucial need in radiology.

AI has the potential to alleviate pressures on healthcare systems by improving diagnostic efficiency and accuracy. By incorporating AI, we can standardize evaluations and reduce inconsistencies in diagnoses, which is especially important for incidental findings in imaging.

I believe that radiologists’ expertise remains indispensable in guiding AI development and application. Collaboration between radiologists and AI professionals is essential to ensure that AI tools are practical, user-friendly, and clinically relevant. Radiologists provide critical feedback that helps refine AI models, ensuring they align with clinical needs.

Our study demonstrates that integrating AI with MR imaging can be a cost-effective strategy. The AI-assisted approach showed lower overall costs and slightly higher quality-adjusted life years when compared to MR imaging alone. This indicates that AI could reduce unnecessary procedures and improve patient outcomes by providing more accurate diagnoses.

Despite the promising results, the adoption of AI in clinical practice faces challenges, including cost and IT integration; ethical, legal, and societal factors also play a role in the slow adoption of AI technologies. Addressing these concerns through collaborative efforts and continuous evaluation is vital to the successful integration of AI in radiology, ultimately improving patient care and outcomes.

Key points:

• This is a model-based study using data from literature where AI has been applied in the diagnostic workup of incidental renal lesions.
• MRI + AI has the potential to be a cost-effective alternative in the differentiation of incidental renal lesions.
• The additional use of AI can reduce costs in the diagnostic workup of incidental renal lesions.

Article: Artificial intelligence support in MR imaging of incidental renal masses: an early health technology assessment

Authors: Alexander W. Marka, Johanna Luitjens, Florian T. Gassert, Lisa Steinhelfer, Egon Burian, Johannes Rübenthaler, Vincent Schwarze, Matthias F. Froelich, Marcus R. Makowski & Felix G. Gassert