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

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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Publication Details

Journal
Osteoporosis International
Published
2026-05-16
DOI
https://doi.org/10.1007/s00198-026-08067-6
Primary Topic
Bone health and osteoporosis research
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Chien‐An Shih, Yuan‐Fu Liu, Ding‐Cheng Chan, Wei‐Chieh Hung et al.
Osteoporosis International
Bone health and osteoporosis research
article

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

Chien‐An Shih, Yuan‐Fu Liu, Ding‐Cheng Chan, Wei‐Chieh Hung, Yoon‐Sok Chung, Tien‐Tsai Cheng, Chung‐Hwan Chen, Fen Lee Hew, Siew Pheng Chan, Wing P. Chan, Keh‐Sung Tsai, Dipendra Pandey, Vilai Kuptniratsaikul, Jawl‐Shan Hwang, Kyu Ri Hwang, C K Ho, Unnop Jaisamrarn, Natthinee Charatcharoenwitthaya, Kwang‐Kyoun Kim, Shau‐Huai Fu, Chun‐Feng Huang, F P Chen, Shih‐Te Tu, Sarath Lekamwasam, Swan Sim Yeap, S. Ferrari, Huei‐Kai Huang, Manju Chandran, Ching‐Chou Tsai, Der‐Sheng Han, Huipeng Shi, Kun-Hui Chen, Yu‐Cheng Chang, C L Wu, Julie Li-Yu, E Michael Lewiecki, Fernando Marín, Ang Seng Bin, Ching Lung Cheung, Tuan Van Nguyen, David Kendler, Sakae Tanaka, Hsuan-Yu Chen, Wen-Hui Fang, Tsung-Han Yang, Akira Taguchi, Satoshi Mori, Ta-Wei Tai, Joon Kiong Lee, Yumie Rhee, Eugene McCloskey, Kenneth G. Saag, Jung-Fu Chen, Lan T. Ho-Pham, Peter R. Ebeling, John J. Carey, Rong-Sen Yang, Didier Hans, Sung-Yen Lin, John A. Kanis
article en

Abstract

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.

Osteoporosis International
Thammasat University (TH), University of Technology Sydney (AU), Chaoyang University of Technology (TW), University of Santo Tomas Hospital (PH), University of Ruhuna (LK), University of British Columbia (CA), National Sun Yat-sen University (TW), National Yang Ming Chiao Tung University (TW), Kaohsiung Medical University (TW), Chulalongkorn University (TH), Konyang University (KR), National Pingtung University of Science and Technology (TW), National Taiwan University (TW), Chang Gung University (TW), Pham Ngoc Thach University of Medicine (VN), Ollscoil na Gaillimhe – University of Galway (IE), Yonsei University (KR), Singapore General Hospital (SG), Mahidol University (TH), Severance Hospital (KR), Tzu Chi Foundation (TW), Seoul Metropolitan Government (KR), Assunta Hospital (MY), Chang Gung Memorial Hospital (TW), University of Alabama at Birmingham (US), University of Malaya (MY), E-Da Hospital (TW), Taichung Veterans General Hospital (TW), Kaohsiung Chang Gung Memorial Hospital (TW), New Mexico Clinical Research & Osteoporosis Center (US), Shanghai Ninth People's Hospital (CN), Far Eastern Memorial Hospital (TW), University Hospital of Geneva (CH), University of Tokyo Hospital (JP), Hospital Universitario Quirónsalud Madrid (ES), Keelung Chang Gung Memorial Hospital (TW), KK Women's and Children's Hospital (SG), Hospital Quirónsalud Barcelona (ES), Duke-NUS Medical School (SG), Seirei Hamamatsu General Hospital (JP), National Taiwan University Hospital (TW), Chi Mei Medical Center (TW), National Academy of Medical Sciences (NP), Wan Fang Hospital (TW), Subang Jaya Medical Centre (MY), Diagnostics for the Real World (United Kingdom) (GB), National Cheng Kung University Hospital (TW), Changhua Christian Hospital (TW), Taipei Medical University (TW), Monash University (AU), Ajou University (KR), National Defense Medical Center (TW), University of Hong Kong (HK), Matsumoto Dental University (JP), University of Sheffield (GB), National Cheng Kung University (TW), University of Lausanne (CH), I-Shou University (TW)
National Science and Technology Council
Openalex Percentile: Top 4%
Bone health and osteoporosis research
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