The Coming Disruption of Fracture Liaison Services in the Era of AI-Driven Imaging Detection Tools
Introduction
The landscape of bone health management is ripe for a seismic shift with the advent of AI-driven imaging technologies. These tools are set to revolutionize osteoporosis diagnosis and fracture risk assessment, fundamentally altering how patients are identified and treated. However, the current Fracture Liaison Service (FLS) model, operating within a fee-for-service healthcare environment, is neither scalable nor sustainable in the face of this impending transformation. Traditionally reactive and post-fracture focused, the FLS model will no longer be sufficient in an era where AI can easily identify at-risk patients before fractures occur. This transition demands a shift from post-fracture care toward a proactive, primary fracture prevention model that is both scalable and integrated into routine clinical practice.
The Inherent Limitations of the Current Fracture Liaison Service Model
Resource Constraints and Workforce Challenges
FLS programs are heavily dependent on specialist nurses, surgeons, and endocrinologists, et.al., to identify and manage osteoporosis in patients who have already suffered a fracture. However, the demand on these healthcare professionals is outpacing available resources, making it impossible to scale this model effectively. With AI-driven imaging detecting thousands of additional at-risk individuals, an already burdened workforce cannot accommodate the growing caseload within the existing system.
A Reactive, Post-Fracture Approach
The FLS model primarily targets patients after they sustain fractures, relying on DXA scans, risk assessments, and clinical evaluations to guide treatment. While this has improved secondary fracture prevention, it fails to address primary fracture prevention—leaving a significant portion of at-risk patients undiagnosed until they break a bone. If implemented at scale, AI-driven imaging will shift osteoporosis detection upstream, requiring an entirely new infrastructure for early intervention and continuous monitoring.
Fragmented Care and Poor Patient Adherence
Under the current system, osteoporosis care is fragmented, often requiring multiple referrals between primary care, endocrinologists, and orthopedic specialists. This creates gaps in patient adherence, with many individuals failing to complete necessary follow-ups, diagnostic tests, or prescribed treatments. AI-powered tools will integrate osteoporosis detection into standard imaging workflows, but unless care delivery is redesigned, millions of newly identified patients will be left without clear treatment pathways.
The U.S. Fee-for-Service Model Reinforces the Status Quo
The current reimbursement model rewards procedures rather than prevention, making it financially unappealing for healthcare providers to focus on proactive bone health management. AI-driven imaging exposes a major misalignment—detecting osteoporosis at scale will not generate incentives unless the system shifts toward value-based care that prioritizes prevention, intervention, and long-term monitoring.
AI-Driven Imaging: A Game-Changer with Significant Challenges
AI’s Efficiency in Identifying Patients at the Front End
AI-driven imaging tools can detect osteoporosis and bone health deterioration at an unprecedented scale, integrating seamlessly into routine X-rays, CT scans, and ultimately MRIs. Unlike DXA scans, which are performed selectively based on risk factors or post-fracture assessments, AI can identify patients far earlier in their healthcare journey, often during unrelated imaging procedures. This will transform osteoporosis detection into a proactive, front-end process, dramatically increasing the number of at-risk patients who require intervention.
Illustration by Nicolas Trottier
The Core Challenge: Managing the Surge in Identified Patients
While AI-powered imaging solves the detection problem, it creates a new challenge: how do we manage the massive influx of newly diagnosed patients? The existing FLS model cannot simply be expanded or overlaid onto this new paradigm—it must be fundamentally redesigned. The healthcare system must transition toward integrated, scalable care pathways that distribute responsibility across primary care, orthopedics, and digital health solutions.
The Need for a Complete Redesign of Bone Health Management
You Cannot Juxtapose a Disruptive Technology onto a Broken Model
AI-driven imaging exposes and amplifies the inefficiencies of the current system. You cannot simply layer an advanced detection technology onto a fragmented, reactive model. The entire bone health management process must be re-engineered from the ground up to handle early detection, intervention, and long-term monitoring.
A New Model: Prioritizing AI in the Orthopedic Workflow
A proactive, scalable model ideally would prioritize AI-driven imaging within the orthopedic workflow, ensuring that bone health is assessed at key clinical touchpoints. This model could include:
Orthopedic-Led AI Screening and Pre-Operative Optimization: AI-driven imaging fully integrated into pre-operative planning for joint replacements, spinal surgeries, and trauma cases. Orthopedic teams that incorporate bone health management into surgical decision-making, ensure that at-risk patients receive early intervention before undergoing major procedures.
Primary Care-Integrated Osteoporosis Screening: Embedding AI-driven imaging results into routine primary care visits will allow for early detection and proactive intervention, reducing the incidence of first fractures.
Integrated Digital Health Platforms: AI-powered risk assessments feed into automated, scalable care pathways, reducing dependence on overwhelmed specialists and ensuring efficient patient management.
Value-Based Reimbursement Models: Financial incentives will ideally nudge the shift toward preventive care, pharmacologic intervention, and lifestyle modifications, encouraging early diagnosis and long-term bone health management.
Seizing the Opportunity to Reinvent Bone Healthcare
AI-driven imaging is poised to revolutionize bone health management, but without systemic redesign, the result will be chaos, bottlenecks, and a growing patient backlog. The challenge isn’t just AI adoption—it’s about creating a new, scalable system that can handle the unprecedented efficiency of AI-driven detection.
Healthcare leaders can act now to:
Fully integrate AI into the orthopedic workflow for early detection and surgical planning.
Expand provider education and training on osteoporosis management.
Align reimbursement incentives with early intervention and long-term bone health.
Develop digital tools for automated patient stratification and follow-up.
The future of bone health is about to change. The question is: will the healthcare system be ready?
