In this comprehensive review, we explore the full spectrum of the most common incidentally found bone lesions in children and adolescents. Non-ossifying fibromas (NOFs) are benign, self-limiting bone lesions that represent a significant proportion of skeletal abnormalities in this population. Beginning with their first description by Sontag and Pyle and the subsequent histological characterization by Jaffe and Lichtenstein, we trace the historical evolution of understanding NOFs, including early theories on etiology, while outlining key epidemiologic, histopathologic, and advanced imaging findings. Furthermore, we discuss diagnostic criteria, management strategies, and emerging molecular insights for NOFs, emphasizing their clinical significance. By integrating historical perspectives, current diagnostic protocols, and emerging discoveries, this paper seeks to assist clinicians in optimizing diagnostic and treatment protocols to prevent unnecessary interventions through a comprehensive understanding of NOFs.

OBJECTIVES: To assess the interrater reliability of ChatGPT-4 to that of orthopaedic surgery attendings and residents in classifying fractures on upper extremity (UE) and lower extremity (LE) radiographs. METHODS: Eighty-four radiographs of various fracture patterns were collected from publicly available online repositories. These images were presented to ChatGPT-4 with the prompt asking it to identify the view, body location, fracture type, and AO/OTA fracture classification. Two orthopaedic surgery residents and two attending orthopaedic surgeons also independently reviewed the images and identified the same categories. Fleiss kappa values were calculated to determine interrater reliability (IRR) for the following: All raters combined, AI versus residents (AIR); AI versus attendings (AIA); and attendings versus residents (AR). RESULTS: ChatGPT-4 achieved substantial to almost perfect agreement with clinicians on location (UE: κ = 0.655–0.708, LE: κ = 0.834–0.909) and fracture type (UE: κ = 0.546–0.563, LE: κ = 0.58–0.697). For view, ChatGPT-4 showed consistent fair agreement for both UE (κ = 0.370–0.404) and LE (κ = 0.309–0.390). ChatGPT-4 struggled the most with AO/OTA classification achieving slight agreement for UE (κ = −0.062 to 0.159) and moderate agreement for LE (κ = 0.418–0.455). IRR for AIR was consistently lower than IRR for AR. For AR comparisons, almost perfect agreement was observed for location (UE: κ = 0.896, LE: κ = 0.912) and fracture type (UE: κ = 0.948, LE: κ = 0.859), whereas AO/OTA classification showed fair agreement for UE (κ = 0.257) and moderate for LE (κ = 0.517). The P values for all comparison groups were significant except for LE AO/OTA classification between AI and residents ( P = 0.051). CONCLUSIONS: Although ChatGPT-4 showed promise in classifying basic fracture features, it was not yet at a level comparable to experts, especially with more nuanced interpretations. These findings suggest that the use of AI is more effective as an adjunct to the judgment of trained clinicians rather than a replacement for it. LEVEL OF EVIDENCE: Diagnostic Level IV. See Instructions for Authors for a complete description of levels of evidence.

In this comprehensive review, we explore the full spectrum of the most common incidentally found bone lesion in children and adolescents. Non-ossifying fibromas (NOFs) are benign, self-limiting bone lesions that represent a significant proportion of skeletal abnormalities in this population. Beginning with their first description by Sontag and Pyle and the subsequent histological characterization by Jaffe and Lichtenstein, we trace the historical evolution of understanding NOFs, including early theories on etiology, while outlining key epidemiologic, histopathologic, and advanced imaging findings. Furthermore, we discuss diagnostic criteria, management strategies, and emerging molecular insights for NOFs, emphasizing their clinical significance. By integrating historical perspectives, current diagnostic protocols, and emerging discoveries, this paper seeks to assist clinicians in optimizing diagnostic and treatment proto-cols to prevent unnecessary interventions through a comprehensive understanding of NOFs.