Editorial discusses the impact of AI in ultrasound, highlighting implementation challenges and the need for oversight.
Artificial intelligence (AI) is reshaping the landscape of diagnostic ultrasound at an unprecedented pace, offering the promise of improved diagnostic consistency, expanded access in resource-limited environments, and enhanced workflow efficiency. Yet enthusiasm must be tempered by a critical appraisal of the field's current limitations, including algorithmic bias, fragmented regulatory frameworks, risks of operator deskilling, and unresolved questions of clinical liability. This editorial argues that AI in ultrasound is not a distant horizon but an unfolding clinical reality, one that demands proactive, profession-led stewardship. Ultrasound societies, journal editors, educators, and clinicians must collectively define the standards, validate frameworks, and create educational curricula that will determine whether this technology fulfils its considerable promise or becomes a source of unintended harm.
No takes yet. Share an insight, caveat, or question.
Manjiri Dighe (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: