Background: The digital health research field is growing rapidly, and a summary of the available digital tools for triaging musculoskeletal conditions is needed. Effective and safe digital triage tools for musculoskeletal conditions could support patients in making informed care decisions, aid clinicians and patients in navigating care, and may contribute to reducing ED overcrowding and healthcare costs. Objective: To identify and describe digital health tools for use by adults to triage musculoskeletal conditions across primary, urgent, or emergency care settings. Methods: Our scoping review was conducted following the Johanna Briggs Institute recommendations for scoping reviews and Arksey & O’Malley’s framework. Systematic searches in MEDLINE (OVID), CINAHL (EBSCO), PsycINFO (EBSCO), Embase (OVID), Cochrane Library, Web of Science, OpenGrey, GoogleScholar, arXiv.org, medRxiv.org, and an extensive grey literature search were conducted with a librarian scientist from inception to Sept 18, 2025. Studies had to recruit adults (18+ years) with musculoskeletal conditions that identified a digital health tool designed to triage or diagnose in primary, urgent, or emergency care settings and report primary data to be included. Two reviewer pairs independently screened abstracts and full-text articles Relevant data were extracted in duplicate, and results were summarized descriptively. Results: The search yielded 5695 records, and we screened 189 full-text articles. Thirty-four studies (n=37,509 patients) met the inclusion criteria. The most common musculoskeletal conditions reported were rheumatoid/inflammatory arthritis (n=13, 38%). Nineteen (59%) studies reported on symptom checkers, 13 (44%) studies on triage/diagnosis tools, and 2 (6%) were studies of diagnostic predictor tools. There were 16 unique digital health tools. Two tools were built for triaging musculoskeletal conditions, and were not publicly available outside the UK National Health Service. Most tools were generic tools designed to screen for general health problems, including musculoskeletal conditions. The most common approach to evaluating performance (eg, accuracy) of the tools was to compare the concordance of the tool to a clinician diagnosis or triage recommendation. Sensitivity and specificity ranged from 39%-91% and 23%-80%, respectively. Reported accuracy of included tools ranged from 33% to 98%. Conclusions: Musculoskeletal conditions remain a blind spot for people designing, implementing, and evaluating digital health for triage: few tools were specifically designed for musculoskeletal conditions, and most existing tools performed poorly when applied to musculoskeletal populations. The evidence base supporting accuracy of digital health for triaging and diagnosing musculoskeletal conditions is weak, and tool performance was inconsistent and lacking transparency. We recommend health systems and clinicians use a multi-modal approach, integrating both digital health tools and clinical decision-making to safely triage and diagnose until a more robust tool for musculoskeletal conditions is available. Future tool developers need to use transparent, standardized processes that prioritize tool safety, clinical value, and trustworthiness when designing for clinicians and patients.