Aims. This Delphi study assessed the challenges of diagnosing soft-tissue knee injuries (STKIs) in acute settings among orthopaedic healthcare stakeholders. Methods. This modified e-Delphi study consisted of three rounds and involved 32 orthopaedic healthcare stakeholders, including physiotherapists, emergency nurse practitioners, sports medicine physicians, radiologists, orthopaedic registrars, and orthopaedic consultants. The perceived importance of diagnostic components relevant to STKIs included patient and external risk factors, clinical signs and symptoms, special clinical tests, and diagnostic imaging methods. Each round required scoring and ranking various items on a ten-point Likert scale. The items were refined as each round progressed. The study produced rankings of perceived importance across the various diagnostic components. Results. In Round 1, the study revealed widespread variability in stakeholder opinions on diagnostic components of STKIs. Round 2 identified patterns in the perceived importance of specific items within each diagnostic component. Round 3 produced rankings of perceived item importance within each diagnostic component. Noteworthy findings include the challenges associated with accurate and readily available diagnostic methods in acute care settings, the consistent acknowledgment of the importance of adopting a patient-centred approach to diagnosis, and the transition from divergent to convergent opinions between Rounds 2 and 3. Conclusion. This study highlights the potential for a paradigm shift in acute STKI diagnosis, where variability in the understanding of STKI diagnostic components may be addressed by establishing a uniform, evidence-based framework for evaluating these injuries. Cite this article: Bone Jt Open 2024;5(11):984–991

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.