Artificial intelligence in orthopaedic surgery.

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.

Unveiling the potential of artificial intelligence in orthopaedic surgery.

Artificial intelligence is paving the way in contemporary medical advances, with the potential to revolutionise orthopaedic surgical care. By harnessing the power of complex algorithms, artificial intelligence yields outputs that have diverse applications including, but not limited to, identifying implants, diagnostic imaging for fracture and tumour recognition, prognostic tools through the use of electronic medical records, assessing arthroplasty outcomes, length of hospital stay and economic costs, monitoring the progress of functional rehabilitation, and innovative surgical training via simulation. However, amid the promising potential and enthusiasm surrounding artificial intelligence, clinicians should understand its limitations, and caution is needed before artificial intelligence-driven tools are introduced to clinical practice.

Diagnosis and management of leptomeningeal disease secondary to grade IV astrocytic glioma.

A man in his mid-40s presented to hospital with confusion, headache and feeling generally unwell. He had had a total resection of a grade IV astrocytic glioma 1 year prior. Initial observations, blood tests and CT head scan were unremarkable for acute features to explain the patient's presentation. However, an MRI head scan on this admission demonstrated a clear communicating hydrocephalus with new abnormal leptomeningeal enhancement, consistent with leptomeningeal metastatic infiltration by glioma. Lumbar puncture cytology and biochemistry supported this interpretation. As a small district general hospital in rural Wales, we discuss the experience of diagnosis and coordination of specialist input from a multidisciplinary team. We share the challenges of managing leptomeningeal disease in the COVID-19 pandemic, in the context of the additional risks this presents with chemotherapy-induced immunosuppression.