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Summary <p>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.</p> Purpose <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月16日

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

摘要

Summary

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.