Aims. Orthopaedic surgery uses many varied instruments with high-speed, high-impact, thermal energy and sometimes heavy instruments, all of which potentially result in aerosolization of contaminated blood, tissue, and bone, raising concerns for clinicians’ health. This study quantifies the aerosol exposure by measuring the number and size distribution of the particles reaching the lead surgeon during key orthopaedic operations. Methods. The aerosol yield from 17 orthopaedic open surgeries (on the knee, hip, and shoulder) was recorded at the position of the lead surgeon using an Aerodynamic Particle Sizer (APS; 0.5 to 20 μm diameter particles) sampling at 1 s time resolution. Through timestamping, detected aerosol was attributed to specific procedures. Results. Diathermy (electrocautery) and oscillating bone saw use had a high aerosol yield (> 100 particles detected per s) consistent with high exposure to aerosol in the respirable range (< 5 µm) for the lead surgeon. Pulsed lavage, reaming, osteotome use, and jig application/removal were medium aerosol yield (10 to 100 particles s. -1. ). However, pulsed lavage aerosol was largely attributed to the saline jet, osteotome use was always brief, and jig application/removal had a large variability in the associated aerosol yield. Suctioning (with/without saline irrigation) had a low aerosol yield (< 10 particles s. -1. ). Most surprisingly, other high-speed procedures, such as drilling and screwing, had low aerosol yields. Conclusion. This work suggests that additional precautions should be recommended for diathermy and bone sawing, such as enhanced personal protective equipment or the use of suction devices to reduce exposure. Cite this article: Bone Joint Res 2023;12(10):636–643

The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as ‘big data’, AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI’s limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction. Cite this article: Bone Joint Res 2023;12(7):447–454