Introduction Three‐dimensional (3D) printed models can be constructed utilising computed tomography (CT) data. This project aimed to determine the effect of changing the slice reconstruction interval (SRI) on the spatial replication accuracy of 3D‐printed anatomical models constructed by fused deposition modelling (FDM). Methods Three bovine vertebrae and an imaging phantom were imaged using a CT scanner. The Queensland State Government’s Animal Care and Protection Act 2001 did not apply as no animals were harmed to carry out scientific activity. The data were reconstructed into SRIs of 0.1, 0.3, 0.5 and 1 mm and processed by software before 3D printing. Specimens and printed models were measured with calipers to calculate mean absolute error prior to statistical analysis. Results Mean absolute error from the original models for the 0.1, 0.3, 0.5 and 1 mm 3D‐printed models was 0.592 ± 0.396 mm, 0.598 ± 0.479 mm, 0.712 ± 0.498 mm and 0.933 ± 0.457 mm, respectively. Paired t‐tests (P < 0.05) indicated a statistically significant difference between all original specimens and corresponding 3D‐printed models except the 0.1 mm vertebrae 2 (P = 0.061), 0.3 mm phantom 1 (P = 0.209) and 0.3 mm vertebrae 2 (P = 0.097). Conclusion This study demonstrated that changing the SRI influences the spatial replication accuracy of 3D‐printed models constructed by FDM. Matching the SRI to the primary spatial resolution limiting factor of acquisition slice width or printer capabilities optimises replication accuracy.

The integration of artificial intelligence (AI) technology within the health industry is increasing. This educational piece discusses the implementation of AI and its impact on sonography. The authors investigate how AI may influence the profession and provide examples of how ultrasound imaging may be enhanced and innovated by integrating AI technology. This article highlights challenges related to the application of AI and provides insight into how they could be addressed. The critical distinction between the role of a sonographer and the reporting specialist in the context of AI is highlighted as a key issue for those developing, researching, and evaluating AI systems. A key recommendation is for the sonography community to address ultrasound education, particularly how AI knowledge could be incorporated into university education. This is an important consideration that should be extended to practising professionals as they may be involved in evaluating the efficiency and methodologies used in new research that may incorporate AI technologies.