From detection to action: Transparency, accuracy, and integration for clinical artificial intelligence in musculoskeletal aging

From detection to action: Transparency, accuracy, and integration for clinical artificial intelligence in musculoskeletal aging A recent collaboration between Vietnam and Taiwan marks the opening of a new chapter in musculoskeletal care.Nearly 2000 pelvic and hip radiographs obtained during routine care in central Vietnam were analyzed by an artificial intelligence (AI) system developed in Taiwan for osteoporosis screening [1].The study illustrates how AI can extract additional clinical information from images obtained during routine care, enabling earlier identification of individuals who might otherwise remain unscreened.AI may accelerate screening, automate selected screening tasks, extend clinical capacity, and support case finding in regions where dual-energy X-ray absorptiometry (DXA) and trained personnel are limited.At the same time, important questions remain about how clinicians should interpret AI outputs, judge their reliability, and connect detection to appropriate care.These questions motivate this Special Issue.The four contributions in this Special Issue show how AI is being explored across musculoskeletal care.They examine automated muscle assessment after hip fracture, osteoporosis screening from chest radiographs, vertebral compression fracture detection, and knee osteoarthritis grading and reporting [2][3][4][5].All use medical images to detect or quantify clinically relevant features of bone, muscle, or joints.Together, they reflect a field that is advancing but is not yet part of routine care in most settings, with uneven study quality and reporting across the broader osteoporosis AI literature [6].For clinicians, the first interpretive pitfall is to equate strong technical performance with a clinically useful test.The area under the receiver operating characteristic curve (AUC) summarizes overall discrimination, but it does not show whether risks are calibrated, whether the threshold fits the clinical purpose, or how many patients will undergo unnecessary testing.The relevant performance measure depends on the intended use and the consequences of false-negative and false-positive results.AI also creates a black-box problem.Explanations may help clinicians assess whether an output is clinically plausible, but they do not replace validation [7].Before acting, clinicians should know the target population and condition, the operating threshold, the uncertainty, the likely consequences of errors, and the required confirmatory assessment.The Asia-Pacific consensus statement provides detailed guidance on the validation, reporting, ethical use, and clinical implementation of AI in osteoporosis care [8].For clinicians, however, practical assessment can begin with three simple principles: transparency, accuracy, and integration.