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Consensus statement on the application of artificial intelligence in osteoporosis screening and management: perspectives from the Asia-Pacific region

3Citations signalées, ce qui n’est pas une note de qualité
59Institutions déclarées
19Pays d’affiliation déclarés

Rattachement africain : tw, sg, my, kr, au, th, np, ch, jp, lk, us, es, ph, ie, ca, gb, hk, vn, cn. Niveau de preuve : code pays fourni par la source.

Le résumé fourni par la source

Osteoporosis is a major and growing health concern in the Asia-Pacific region, y et it remains widely underdiagnosed and undertreated due to limited access to dual-energy X-ray absorptiometry (DXA) in many areas. Artificial intelligence (AI) offers new opportunities to improve osteoporosis screening and management, but unvalidated tools pose risks of inconsistent care. This consensus was developed to provide regionally harmonized guidance on the safe, effective, and equitable use of AI in osteoporosis care. PURPOSE: The aim of this work was to establish expert consensus recommendations on the role of AI in osteoporosis screening and management in the Asia-Pacific region. Key objectives were to define appropriate applications of AI (e.g., imaging-based bone assessment and fracture risk prediction) and specify minimum standards for validation and reporting, addressing region-specific implementation challenges and ensuring that AI use aligns with clinical guidelines and ethical principles. METHODS: This consensus was developed through multidisciplinary collaboration among experts across the Asia-Pacific region. Each participant reviewed draft statements, contributed feedback during virtual meetings, and provided insights based on clinical experience and current evidence. Consensus was reached iteratively until full agreement was achieved for all statements. The process integrated global best practices and regional adaptations, drawing from peer-reviewed studies, international AI guidelines, and local fracture registry data. The final recommendations emphasize the validation, transparency, and ethical implementation of AI within regional healthcare systems, ensuring compatibility with local regulations. Ultimately, twelve consensus statements were established to guide the responsible use of AI for osteoporosis screening and management in the Asia-Pacific region. RESULTS: The panel produced 12 consensus statements covering the role of AI as an adjunct for opportunistic osteoporosis screening rather than a diagnostic tool, requirements for imaging quality and AI model transparency, standards for validation and performance reporting, integration of AI with clinical risk stratification, demonstration of clinical utility in real-world settings, adherence to data protection laws and ethical AI principles, training of clinicians in AI use, strategies for implementation and monitoring (including post-market surveillance and feedback loops), and recognition of technical, clinical, and equity limitations of AI. All 12 statements give extensive recommendations for using AI to improve osteoporosis management while ensuring patient safety, accuracy, and equity. CONCLUSION: This first Asia-Pacific consensus on AI in osteoporosis concludes that AI, when appropriately validated and implemented, can help bridge the osteoporosis care gap by identifying high-risk patients who would otherwise remain undiagnosed, thus facilitating earlier intervention. It emphasizes that AI should complement-not replace-standard diagnostic methods and clinical judgment. The guidance emphasizes validation, transparency, and ethical oversight to facilitate early intervention while minimizing risks associated with unvalidated or premature AI adoption.

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Le contrôle bibliographique ouvert

DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.

Titre Crossref
Consensus statement on the application of artificial intelligence in osteoporosis screening and management: perspectives from the Asia-Pacific region
Date Crossref
01/05/2026
Éditeur
Springer Science and Business Media LLC
Type
journal-article

Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.

Les institutions déclarées

Chaoyang University of TechnologyNational Yang Ming Chiao Tung UniversityChi Mei Medical CenterNational Defense Medical CenterTaichung Veterans General HospitalKaohsiung Medical UniversityChang Gung Memorial HospitalNational Cheng Kung University HospitalChang Gung UniversityKaohsiung Chang Gung Memorial HospitalChanghua Christian HospitalNational Taiwan University HospitalFar Eastern Memorial HospitalKeelung Chang Gung Memorial HospitalE-Da HospitalI-Shou UniversityNational Taiwan UniversitySingapore General HospitalKK Women's and Children's HospitalDuke-NUS Medical SchoolAssunta HospitalSubang Jaya Medical CentreAjou University Medical CenterAjou UniversityKonyang UniversityMonash UniversityChulalongkorn UniversityNational Academy of Medical SciencesUniversity Hospital of GenevaThammasat UniversityMatsumoto Dental UniversityUniversity of RuhunaUniversity of Technology SydneyNew Mexico Clinical Research & Osteoporosis CenterUniversity of Alabama at BirminghamHospital Universitario Quirónsalud MadridHospital Quirónsalud BarcelonaSeirei Hamamatsu General HospitalSeoul Metropolitan GovernmentUniversity of Santo Tomas HospitalOllscoil na Gaillimhe – University of GalwayUniversity of British ColumbiaDiagnostics for the Real World (United Kingdom)University of Hong KongTzu Chi FoundationMahidol UniversityWan Fang HospitalTaipei Medical UniversityUniversity of MalayaPham Ngoc Thach University of MedicineShanghai Ninth People's HospitalYonsei UniversitySeverance HospitalUniversity of SheffieldUniversity of Tokyo HospitalUniversity of LausanneNational Sun Yat-sen UniversityNational Pingtung University of Science and TechnologyNational Cheng Kung University

Une affiliation ne permet pas de déduire la nationalité d’un auteur.

Les sujets associés

Bone health and osteoporosis researchArtificial Intelligence in Healthcare and EducationBone Metabolism and Diseases

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