To the Editor:
We congratulate Gungor et al.  on their work. We agree that acquisition and interpretation of optimal images for ultrasound-guided regional anesthesia (UGRA) is challenging and innovative approaches are required .
We note the statement “clinical validation of the accuracy of a real-time sonoanatomy identification via AI-supported ultrasound-guided peripheral nerve block practice has not been conducted yet”. We put forward our recent publication, Bowness et al. , which presents a clinical evaluation of a real-time AI-based system (ScanNav Anatomy Peripheral Nerve Block; Intelligent Ultrasound, Cardiff) for assisting in the identification of anatomical structures on ultrasound during UGRA. We are also interested that a radiologist was a validator in their study, as radiologists may be less familiar than experienced regional anesthesiologists in identifying sonoanatomy relevant to UGRA.
We fully support the aims of Gungor et al. and look forward to further publications and insights into this exciting field within UGRA. AI is as the beginning of its journey in UGRA—it is vital that we collaboratively build the correct framework and expectations for this promising tool.
Gungor I, Gunaydin B, Oktar SO, Buyukgebiz BM, Bagcaz S, Ozdemir MG, Inan G. A real-time anatomy identification via tool based on artificial intelligence for ultrasound-guided peripheral nerve block procedures: an accuracy study. J Anesth. 2021. https://doi.org/10.1007/s00540-021-02947-3.
Bowness J, El-Boghdadly K, Burckett-St LD. Artificial intelligence for image interpretation in ultrasound-guided regional anaesthesia. Anaesthesia. 2021;76(5):602–7.
Bowness J, Varsou O, Turbitt L, Burckett-St Laurent D. Identifying anatomical structures on ultrasound: assistive artificial intelligence in ultrasound-guided regional anesthesia. Clin Anat. 2021. https://doi.org/10.1002/ca.23742.
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Bowness, J., Laurent, D.BS. AI real-time color overlay of sonoanatomy. J Anesth 35, 602 (2021). https://doi.org/10.1007/s00540-021-02958-0