人工智能、可解释深度学习与生成模型用于骨质疏松症及骨病预测：系统综述与未来研究路线图。
完整摘要 · 中文译文
骨质疏松症是一个重大的全球健康负担，具有相当高的发病率、死亡率和医疗保健成本。人工智能（AI）为骨质疏松症筛查、骨密度定量、骨折风险预测和临床决策支持开辟了新途径，但相关进展在机器学习、深度学习、可解释人工智能（XAI）、生成模型和多模态学习之间仍然较为分散。本系统综述将这些研究线索整合起来，并确定了基于人工智能的骨质疏松症预测的优先研究需求。遵循PRISMA 2020声明，研究从Scopus和Google Scholar中检索，发表时间为2020年1月至最终检索日期2026年6月30日，由两名评价者独立筛选，并通过协商一致解决分歧。每项研究均记录了与设计相关的偏倚风险指标，如回顾性与前瞻性设计、验证策略和参考标准；完整的PROBAST、QUADAS-2和纽卡斯尔-渥太华量表评估已计划但尚未完成。58项研究符合纳入标准，鉴于存在显著异质性，采用叙述性和描述性方式进行综合，未进行正式荟萃分析。研究结果涵盖五个主题：经典机器学习、用于影像的深度学习、可解释人工智能、生成模型和多模态学习。深度学习取得了较高的预测准确性（AUC 0.82-0.96），可解释人工智能提高了临床可解释性，生成模型有助于弥补数据稀缺问题，多模态方法通过结合影像和临床数据提升了性能。 数据。关键空白仍然存在，包括外部验证有限、严重依赖回顾性数据、泛化性差以及监管准备不足。在这些发现的基础上，本综述提出了MXGNet——一个多模态、可解释的生成式框架，纳入隐私保护联邦学习，旨在指导开发稳健、可解释、可临床部署的骨质疏松症预测人工智能工具。
完整摘要 · 英文原文
Osteoporosis is a major global health burden, with considerable morbidity, mortality, and healthcare costs. Artificial intelligence (AI) has opened new avenues for osteoporosis screening, bone mineral density quantification, fracture-risk prediction, and clinical decision support, but progress remains fragmented across machine learning, deep learning, explainable AI (XAI), generative models, and multimodal learning. This systematic review brings these strands together and identifies priority research needs for AI-based osteoporosis prediction. Following the PRISMA 2020 statement, studies were identified from Scopus and Google Scholar, published between January 2020 and the final search date of 30 June 2026, with two reviewers screening independently and resolving disagreements by consensus. Design-related risk-of-bias indicators, such as retrospective vs. prospective design, validation strategy, and reference standard, were recorded for every study; a full PROBAST, QUADAS-2, and Newcastle-Ottawa Scale appraisal is planned but not yet complete. Fifty-eight studies met the inclusion criteria and were synthesized narratively and descriptively, without formal meta-analysis, given substantial heterogeneity. Findings spanned five themes: classical machine learning, deep learning for imaging, explainable AI, generative models, and multimodal learning. Deep learning achieved strong predictive accuracy (AUC 0.82-0.96), XAI improved clinical interpretability, generative models helped offset data scarcity, and multimodal approaches boosted performance by combining imaging and clinical data. Key gaps remain, including limited external validation, heavy reliance on retrospective data, poor generalisability, and limited regulatory readiness. Building on these findings, this review proposes MXGNet, a multimodal, explainable, generative framework incorporating privacy-preserving federated learning, intended to guide development of robust, interpretable, clinically deployable AI tools for osteoporosis prediction.