MINI REVIEW article
Front. Artif. Intell.
Sec. Medicine and Public Health
The Application Progress of Artificial Intelligence in Osteoporosis Diagnosis
Provisionally accepted
- 1School of Pharmacy, Liaoning University of Traditional Chinese Medicine, Da Lian, China
- 2Liaoning University of Traditional Chinese Medicine Affiliated Hospital, Shenyang, China
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Osteoporosis (OP) is a systemic bone metabolic disorder characterized by a decrease in bone mineral density (BMD) and damage to the trabecular bone microarchitecture. With the increasing global aging population, the incidence of OP has been rising annually, particularly among elderly women, making it a significant public health issue. Traditional diagnostic methods such as dual-energy X-ray absorptiometry (DXA), quantitative computed tomography (QCT), and magnetic resonance imaging (MRI) are effective, but they also have certain limitations. Artificial intelligence (AI) technology is playing an increasingly important role in the management of osteoporosis. Through machine learning (ML), image processing, and data analysis, AI can accurately assess bone density, fracture risk, and other factors, improving the early diagnosis rate of OP and providing strong decision support for clinicians to optimize treatment plans and enhance treatment outcomes. However, it also faces challenges such as AI model interpretability, insufficient diversity in training data, lack of clinical validation, and issues related to privacy protection and ethics. Addressing these problems is crucial for promoting the widespread application of AI technology in this field. As technology continues to advance, AI will become an indispensable part of OP research and clinical applications, driving the development of personalized treatment and precision medicine.
Keywords: Osteoporosis, artificial intelligence, machine learning, Fracture risk, diagnosis
Received: 10 Sep 2025; Accepted: 10 Nov 2025.
Copyright: © 2025 Zhang, Tai, Kang and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Keda Li, kodar777@163.com
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