## 4 Future Perspectives: Artificial Intelligence  
In the rapidly evolving landscape of osteoporosis diagnosis and imaging, future perspectives hold promising developments, particularly with the integration of advanced technologies such as artificial intelligence (AI). This is a branch of computer science focused on developing systems that can perform tasks that typically require human intelligence [40; 41; 42; 43]. In various fields, AI algorithms analyze data, learn patterns, and make predictions, offering solutions ranging from image recognition and natural language processing to complex problem solving. In healthcare, AI is increasingly utilized for diagnosis, prognosis, and treatment optimization, showcasing its potential to enhance efficiency and accuracy in medical applications [44; 45; 46]. DECT, as discussed earlier, showcases potential for precise osteoporosis risk assessment. Incorporating AI algorithms into DECT analysis could further refine predictive models, leveraging machine learning to enhance fracture risk predictions based on intricate bone composition details captured by DECT. On the other hand, strategies applied to single-energy CT can have broader and more extensive applications, including the analysis of already acquired images and follow-up assessments. Furthermore, ongoing research, as exemplified by [PERSON] et al., emphasizes the potential of DECT-based algorithms for volumetric BMD assessments [36]. The automated identification of trabecular bone components and the application of biophysical models could revolutionize BMD evaluations, offering more nuanced insights into bone health and fracture susceptibility. On this perspective, the integration of AI into traditional and advanced imaging modalities opens avenues for automated ROI selection, precise material breakdown, and improved diagnostic accuracy, possibly streamlining the assessment process, reducing the time-consuming nature, and ensuring consistency across multiple regions [41; 47]. Opportunistic osteoporosis screening could be performed on CT images acquired for other purposes. On this respect, [PERSON] et al. recently reported, using an AI-based solution, that attenuation values in thoracic and first lumbar vertebrae, particularly in menopausal women, showed a strong correlation with osteopenia and osteoporosis risk. A 10 HU increase in CT values significantly reduced this risk. The combined diagnostic efficacy of all thoracic vertebrae surpasses that of a single verteb, providing a valuable, radiation-efficient method to identify, using the opportunistic approach, high-risk individuals, possibly reducing fracture incidence [48]. Another recent and intriguing perspective involves a predictive model designed to detect osteoporosis using radiomic features extracted from lumbar spine CT images [49]. This radiomic model demonstrated excellent performance, with an area under the curve of 0.994 (95% confidence interval: 0.979-1.00), effectively distinguishing between normal BMD and osteoporosis in lumbar spine CT images [49]. Moreover, AI can be applied also to MRI to detect osteoporosis. For instance, [PERSON] et al. reported the results of a prospective case--control study involving women with and without fragility bone fractures that employed 15 machine learning classifiers on 3T 3D FLASH MRI data [50]. This study underscored the potential of machine learning in predicting osteoporosis and fractures on MRI. Looking ahead, the use of AI in osteoporosis imaging is not only limited to diagnostic applications but extends to predictive analytics. As technological capabilities advance, AI models may evolve to predict individualized fracture risk based on comprehensive imaging data, patient demographics, and clinical history. Consideration should also be given to recent technical developments in imaging acquisition. On this respect, there have been recent attempts to diagnose osteoporosis using MR-based proton density fat fraction (PDFF) measurements.