Artificial intelligence in medical imaging: from bench to bedside

Axel Wismüller

doi:10.1117/12.3025553

4 October 2024 Artificial intelligence in medical imaging: from bench to bedside

Axel Wismüller

Author Affiliations +

Proceedings Volume PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024; PC131180K (2024) https://doi.org/10.1117/12.3025553
Event: Nanoscience + Engineering, 2024, San Diego, California, United States

Abstract

The integration of artificial intelligence (AI) in healthcare, particularly using advanced deep learning and generative methods, presents substantial challenges and opportunities. Implementation of AI in clinical settings, such as radiology, requires evaluation beyond algorithm development. Measuring the effectiveness of AI is complex: should success metrics be based on the number of AI uses in radiology, the variety of pathologies analyzed, throughput, user engagement, or clinical quality measures like diagnostic accuracy or study turnaround time? Economic factors like return on investment are also critical. The author, drawing on multi-year practical experience in large-scale AI deployment in radiology, proposes methods to evaluate and enhance the value of AI in medical imaging. These include AI-PROBE, which assesses radiologists' performance with and without AI, and can provide advances like revised radiology Quality Assurance (QA) programs, or improved multi-institutional public health tracking. The presentation will conclude with introducing a multi-speaker session, covering in-house AI system development, the use of AI in clinical research, and FDA regulatory aspects.

Conference Presentation

Citation Download Citation

Axel Wismüller "Artificial intelligence in medical imaging: from bench to bedside", Proc. SPIE PC13118, Emerging Topics in Artificial Intelligence (ETAI) 2024, PC131180K (4 October 2024); https://doi.org/10.1117/12.3025553

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KEYWORDS

Artificial intelligence

Clinical practice

Medical imaging

Radiology

Medicine

Diagnostics

Image analysis

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