Artificial intelligence in Ultrasound: Pearls and pitfalls in 2024

Abstract

During the last 5 years, artificial intelligence (AI) emerged as a revolutionary tool with significant implications across the healthcare system, heavily influencing scientific research and different medical fields including the disciplines of pathology, oncology and radiology at most. Ultrasound, a cornerstone of medical diagnostics, is also witnessing a transformative process. Recent advancements in AI tools are in fact starting to radically change this field. Interestingly, in a context where the reliance on the expertise of sonographers has been often considered as a major limitation, ultrasound represents an ideal candidate to receive significant benefits from the integration of AI, with promises of enhanced diagnostic accuracy, improved workflow efficiency, and expanded access to high-quality care. Additionally, it should be considered that ultrasound is known to be a useful first diagnostic approach or a screening tool for many medical conditions (e. g. screening for breast lesions, screening for abdominal aortic aneurysm, screening for thyroid nodules, surveillance of patients at risk of hepatocellular carcinoma, screening for carotid or lower limb arteries, atherosclerotic disease, etc.). However, its use cannot be planned widely enough to satisfy such needs that involve very large populations, mainly due to the limited availability and costs of expert medical manpower. Automated use of ultrasound, guided by AI recognition, may have the potential to speed up at least some of such processes reducing the needed manpower. Altogether, the impact of AI in ultrasound is multifaceted, affecting different phases of the examination, ranging from image acquisition, recognition of abnormalities and interpretation to decision-making and patient outcomes; however, some peculiarities of AI tools may slow down its application in clinical practice and must be known to avoid troublesome situations. Hereinafter, we will discuss the major benefits correlated to the introduction of different AI tools in ultrasonology and some of the obstacles that need to be overcome.

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