A first Asia-Pacific consensus on AI in osteoporosis produced 12 consensus statements recommending AI as an adjunct for opportunistic screening — not a standalone diagnostic tool — to help address widespread underdiagnosis where DXA access is limited. The paper concludes that appropriately validated, transparent AI could identify high-risk patients earlier, but should complement standard diagnostics and clinical judgment rather than replace them.

Why It Matters To Your Practice

• Osteoporosis remains underdiagnosed and undertreated across much of the Asia-Pacific region, in part because DXA access is limited.

• AI may help flag patients at high fracture risk during routine care, creating opportunities for earlier workup and treatment.

• For clinicians, the key message is practical: AI can extend screening reach, but only if tools are validated and used within established care pathways.

Clinical Implications

• Use AI for opportunistic screening and risk stratification, not as a replacement for diagnostic standards.

• Confirm AI-flagged findings with appropriate imaging, clinical assessment, and guideline-based evaluation.

• Expect minimum standards around image quality, model transparency, performance reporting, and real-world validation before adoption.

• Implementation should include clinician training, monitoring, feedback loops, and post-market surveillance.

Insights

• The consensus emphasizes that unvalidated AI could worsen inconsistency in care rather than reduce it.

• Recommendations also address ethics, data protection, and equity — especially important in settings with uneven access to imaging and digital infrastructure.

• Regional adaptation matters: tools should fit local regulations, fracture registry data, and healthcare workflows.

The Bottom Line

• AI could help narrow the osteoporosis care gap by finding patients who might otherwise be missed.

• But the safest near-term role is decision support: AI should support, not supplant, clinician judgment in osteoporosis care.