Optimizing the power of AI for fracture detection: from blind spots to breakthroughs.
Authors
Affiliations (6)
Affiliations (6)
- , Tehran, Iran.
- Department of Internal Medicine, University of Virginia Hospital, Charlottesville, VA, 22903, USA.
- Yale School of Medicine, New Haven, CT, 06510, USA.
- Department of Radiology, Cedars Sinai Hospital, Los Angeles, CA, 90048, USA. [email protected].
- Department of Radiology, University of California San Diego (UCSD), San Diego, CA, 92093, USA. [email protected].
- Department of Radiology Division, University of California San Diego (UCSD), 9427 Health Sciences Dr, La Jolla, CA, 92037, USA. [email protected].
Abstract
Artificial Intelligence (AI) is increasingly being integrated into the field of musculoskeletal (MSK) radiology, from research methods to routine clinical practice. Within the field of fracture detection, AI is allowing for precision and speed previously unimaginable. Yet, AI's decision-making processes are sometimes wrought with deficiencies, undermining trust, hindering accountability, and compromising diagnostic precision. To make AI a trusted ally for radiologists, we recommend incorporating clinical history, rationalizing AI decisions by explainable AI (XAI) techniques, increasing the variety and scale of training data to approach the complexity of a clinical situation, and active interactions between clinicians and developers. By bridging these gaps, the true potential of AI can be unlocked, enhancing patient outcomes and fundamentally transforming radiology through a harmonious integration of human expertise and intelligent technology. In this article, we aim to examine the factors contributing to AI inaccuracies and offer recommendations to address these challenges-benefiting both radiologists and developers striving to improve future algorithms.