The prevalence of osteoporosis is one of the most common skeletal problems in the world, which is defined bydiminished bone mineral density (BMD), bone microarchitecture and an elevated risk of fragility fracture. Earlydiagnosis is crucial for avoiding major complications, lowering health care costs, and better patient outcomes.Conventional methods of diagnosis like dual-energy X-ray absorptiometry (DXA), however, face problems ofaccessibility, cost, delayed screening and difficulty in interpretation of data in the clinical setting. In recent years, wehave noticed the great promise of novel approaches such as AI, deep learning and XDL developed to detectosteoporosis through automatic processing of medical images and integration of multimodal clinical information. Thiswork introduces an Explainable Deep Learning Model for Early Osteoporosis Detection by Medical Imaging andClinical Acceptance Analysis framework where Osteofy is the main case study conducted.In addition, the studyinvestigates the ability of consultants to interpret, rely on and use the proposed Osteofy framework and their level ofconfidence in its diagnostic value. Previous studies show that with the use of an explainable multimodal AI system,the accuracy of osteoporosis screening is significantly improved and the physician's trust in automatic predictions isincreased. Likewise, explanatory models based on hip radiographs, facial images, computed tomography andelectronic health records have demonstrated an impressive ability for diagnosis in a transparent and reproducible way.Her work builds off this innovation and seeks to close the gap between top-performing AI algorithms and real-worlddeployment.