Artificial Intelligence-Guided Diagnosis for High-Risk Osteoporosis Populations: A Pragmatic Randomized Clinical Trial

Project Summary

I. Project Objectives

With the rapid advancement of medical technology, smart medical devices have become one of the key components of modern healthcare. However, integrating these emerging technologies into the national health insurance (NHI) reimbursement system while ensuring their clinical value and economic benefits remains a major challenge worldwide.

The primary goal of this project is to assist commercialized smart medical device products-those that have passed TFDA review and seek NHI reimbursement-in conducting comprehensive evaluations of their clinical effectiveness and medical economic impact. Through scientific data and standardized impact assessment procedures, the project aims to provide localized evidence to support reimbursement policy decisions and facilitate the market adoption of smart medical technologies. Ultimately, this project seeks to balance therapeutic efficacy and cost control, offering a scientific foundation for NHI decision-making and paving the way for the sustainable development of AI-driven healthcare innovations.

II. Implementation Methods

1. Multi-center Collaborative Network

   This project will be led by Taichung Veterans General Hospital (TCVGH) as the principal site, with collaboration from Kaohsiung Medical University Chung-Ho Memorial Hospital and Changhua Show Chwan Memorial Hospital. This cross-institutional, cross-regional alliance ensures diverse clinical samples, enhances the representativeness of study results, and allows evaluation of AI medical devices across different healthcare systems and environments.

2. Clinical Trial Design and Implementation for Smart Medical Devices

   The project will design and execute systematic clinical trials for smart medical devices using methodologies such as randomized controlled trials (RCTs), before-and-after studies, pragmatic RCTs (PCTs), cluster RCTs, and stepped-wedge RCTs. These rigorous designs will ensure scientific validity, reproducibility, and practical feasibility in real-world clinical settings.

3. Health Economic Evaluation

   A key component of this project is the medical economic assessment, conducted by experienced health economists through cost-effectiveness analysis. The evaluation will focus on how smart medical devices reduce healthcare costs, improve diagnostic efficiency, and enhance treatment outcomes, quantifying their economic value within the NHI system. This evidence will guide policy makers in making data-driven reimbursement decisions.

4. Standardized Impact Assessment Process

   To ensure high-quality research, the project will establish a comprehensive standardized impact assessment framework covering trial design, data collection, statistical analysis, economic evaluation, and ethical review. This standardized approach not only improves the precision of the study but also accelerates the clinical translation of AI medical devices through streamlined application and review processes.

5. Research Case Study and Clinical Application

   The featured case study in this project is "VeriOsteo OP® Smart Bone Screening System," which targets early osteoporosis screening among adults aged 40 to 80 years in high-risk groups. This study will evaluate the clinical accuracy of AI-based osteoporosis screening and assess its economic contribution to healthcare cost reduction. The findings will directly inform the Ministry of Health and Welfare's NHI Administration in formulating reimbursement standards for AI medical devices.

6. Data Sharing and Information Security

   Throughout the project, all research data collection, exchange, and sharing will strictly adhere to cybersecurity and privacy regulations. The AI models involved will undergo validation to ensure the reliability and scientific rigor of the results. Furthermore, the project will promote collaboration between manufacturers and healthcare institutions to support the adoption of smart medical technologies.

7. Final Outcomes and Future Development

The final deliverables will include a comprehensive evaluation report on the clinical and economic performance of the AI medical device, along with recommendations for NHI reimbursement application. The results will provide a reference model for future AI medical devices entering the reimbursement system and further advance the field of smart healthcare. In the long term, the center aims to expand its research to other disease domains while strengthening data security and ethical oversight to ensure the feasibility and credibility of AI applications in clinical practice.

III. Expected Outcomes and Future Vision

By implementing a multi-center collaborative framework, this project aims to promote clinical adoption and economic evaluation of smart medical devices, offering concrete data to support NHI policy-making. Over time, the project is expected to establish a robust evaluation system for AI medical devices, facilitate broader market adoption, and enhance patient outcomes whi

Study Overview

• Status: Recruiting
• Conditions: Osteoporosis
• Intervention / Treatment: Device: VeriOsteo OP

• Study Type: Interventional
• Enrollment (Estimated): 1180
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: WU WU ZHAN, M.B.A.; Phone Number: 04-23592525#3973; Email: tcvghaicenter@vghtc.gov.tw

Study Locations

• Taiwan
  • Changhua, Taiwan
    • Recruiting
    • Show Chwan Memorial Hospital
    • Contact: Chu Pei-Yi, MD; Phone Number: 0975611855; Email: chu.peiyi@msa.hinet.net
  • Kaohsiung City, Taiwan
    • Recruiting
    • Kaohsiung Medical University Chung-Ho Memorial Hospital
    • Contact: Jaw Yuan Wang, MD; Phone Number: 07-3121101; Email: 1030771@kmuh.org.tw
  • Taichung, Taiwan
    • Recruiting
    • Taichung Veterans General Hospital
    • Contact: Kun Hui Chen, MD; Phone Number: 04-23592525#5100; Email: khc@vghtc.gov.tw

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

Description

Inclusion Criteria:

• Aged between 40 to 80 years old
• Identified as high-risk by the Osteoporosis Self-Assessment Tool for Taiwan Postmenopausal Women (OSTAi) <-1 or Male Osteoporosis Self-Assessment Tool for Taiwan (MOSTAi) ≦11 based on the individual's age and weight (kg)
• Had chest x-ray within one year

Exclusion Criteria:

• Age < 40 years old
• Age >80 years old
• BMI< 18 kg/m2or >30 kg/m2
• Pregnant in prior one year
• Recorded diagnosis of osteoporosis within the past two years
• History of prior DXA imaging (prior quantification of BMD) within 2 years
• History of metabolic bone disease

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Single

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Intervention Group; Participants will receive AI-guided diagnosis for osteoporosis risk assessment. This group follows a 2:1 randomization ratio.	Device: VeriOsteo OP; Participants in the intervention group undergo VeriOsteo OP AI assessment followed by DXA confirmation. The AI system generates a diagnostic report identifying individuals at high risk for osteoporosis. Results are used to guide clinical decision-making and evaluate the diagnostic consistency between the AI-guided system and the gold-standard DXA assessment.
No Intervention: Control Group; Participants will receive standard-of-care (routine diagnosis) for osteoporosis assessment.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
The primary objective is to evaluate the clinical effectiveness in terms of the improvement of BMD, of AI-guided diagnosis (VeriOsteo OP)	The primary endpoint of this study is the change in Bone Mineral Density (BMD) expressed as a T-score, measured by Dual-energy X-ray Absorptiometry (DXA). The T-score is a standardized metric comparing bone density to a healthy young adult. While there is no theoretical absolute minimum or maximum value, typical clinical values range from -5.0 to +2.0. According to the International Osteoporosis Foundation (IOF) and Taiwan 2020 guidelines, a T-score of -1.0 or higher is normal, between -1.0 and -2.5 indicates osteopenia, and a T-score of -2.5 or lower (≤ -2.5) indicates osteoporosis. Therefore, a higher (more positive) T-score represents a better outcome (higher bone density). To avoid inflating the detection rate for osteoporosis based on the protocolized DXA at baseline in the intervention arm, a positive AI screen was required to be present to count the chest X-ray findings for the purpose of the primary end point.	Up to the end of follow-up (average 18 months)

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Incidence of Bone Fractures	he incidence of bone fractures, including hip fracture, vertebral fracture, wrist fracture, and other fractures. Bone fractures will be identified from the participant interviews and confirmed by International Classification of Diseases (ICD) codes extracted from electronic health records (EHRs) across the three study sites.	Every 6 months up to 18 months
All-Cause Mortality	The incidence of all-cause death. Participant status will be followed by telephone and verified through electronic health records (EHRs) from the three study sites.	Every 6 months up to 18 months

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Osteoporosis-related healthcare resource utilization	To analyze the total number of healthcare resource utilizations related to osteoporosis, including the combined counts of outpatient visits, emergency department visits, and hospitalizations. Data will be collected through a structured questionnaire via telephone interview.	12th month
Osteoporosis-related medical costs	To evaluate the direct medical costs associated with osteoporosis healthcare resource utilization (expressed in New Taiwan Dollars (TWD ). Data will be collected through a structured questionnaire via telephone interview.	12th month

Collaborators and Investigators

• Sponsor: Taichung Veterans General Hospital
• Collaborators: Ministry of Health and Welfare, Taiwan
• Investigators: Principal Investigator: Kun Hui Chen, MD, Taichung Veterans General Hospital

Publications and helpful links

Helpful Links

• Related Info

Study record dates

Study Major Dates

• Study Start (Actual): March 13, 2026
• Primary Completion (Estimated): June 17, 2026
• Study Completion (Estimated): November 12, 2026

Study Registration Dates

• First Submitted: May 14, 2026
• First Submitted That Met QC Criteria: May 27, 2026
• First Posted (Actual): June 2, 2026

Study Record Updates

• Last Update Posted (Actual): June 2, 2026
• Last Update Submitted That Met QC Criteria: May 27, 2026
• Last Verified: May 1, 2026

More Information

Terms related to this study

• Keywords: Health Economics; Software as a Medical Device (SaMD); Clinical Impact Analysis; Standardization of NHI Reimbursement
• Additional Relevant MeSH Terms: Bone Diseases; Musculoskeletal Diseases; Metabolic Diseases; Bone Diseases, Metabolic; Nutritional and Metabolic Diseases; Osteoporosis
• Other Study ID Numbers: SC251020B

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: As this is a multi-center study, the sharing of individual participant data (IPD) would require joint approval from the Institutional Review Boards (IRBs) of all three participating hospitals. Given the complex regulatory procedures and strict privacy protocols involved, the data will not be made publicly available at this time.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No