Artificial intelligence and computer vision in orthopaedic trauma

Abstract

Artificial intelligence (AI) is, in essence, the concept of ‘computer thinking’, encompassing methods that train computers to perform and learn from executing certain tasks, called machine learning, and methods to build intricate computer models that both learn and adapt, called complex neural networks. Computer vision is a function of AI by which machine learning and complex neural networks can be applied to enable computers to capture, analyze, and interpret information from clinical images and visual inputs. This annotation summarizes key considerations and future perspectives concerning computer vision, questioning the need for this technology (the ‘why’), the current applications (the ‘what’), and the approach to unlocking its full potential (the ‘how’). Cite this article: Bone Joint J 2022;104-B(8):911–914.

The impact of the European Union’s Medical Device Regulation on orthopaedic implants, technology, and future innovation

“…18 Recent advances in artificial intelligence-based algorithms, predictive analytics, virtual reality, smart devices, and robotic arm-assisted surgery are proving to be invaluable adjuncts across various orthopaedic subspecialties. [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38] Previously, the MDD could give fast-track approvals for these "Software as Medical Device" (SaMD) applications using substantial equivalence to existing products. However, it has been suggested that this fast-track application system created a loophole in the certification process for some technological innovations, with premature implementation of SaMD applications that compromised patient safety.…”

Artificial intelligence in orthopaedic surgery

“…3,37 AI is as good as its data, and the development of robust reporting frameworks is vital to preventing avoidable errors. 70,[76][77][78] Guidelines for establishing models are necessary, such as the Transparent Reporting of a multivariate prediction model for Individual Prognosis or Diagnosis (TRIPOD) initiative, 77 which has already been used in validating ML in orthopaedics. The complexity of CNNs usually depends upon the complexity of the input data.…”

Looking back over the past year

“…[2][3][4][5][6] Their message should be heeded by all readers, regardless of their subspecialty. [7][8][9][10] If we are to see benefits for our patients, the questions addressed must be meaningful, the methodology must be rigorous, and the results must be reported transparently, appropriate data must be available, and each model of AI must be externally validated. The paper by Polisetty et al 1 and their recommendations, allied to our previous guidance, will be critical to the future evaluation and use of AI in arthroplasty surgery.…”