Shoulder pain is a common complaint in primary care settings. The prevalence of shoulder pain is on the rise, especially in societies with aging populations. Like other joint-related conditions, shoulder pain is predominantly caused by degenerative diseases. These degenerative changes typically affect bones, tendons, and cartilage, with common conditions including degenerative rotator cuff tears, impingement syndrome, and osteoarthritis. Diagnosing these degenerative diseases in older adults requires a thorough understanding of basic anatomy, general physical examination techniques, and specific diagnostic tests. This review aims to outline the fundamental physical examination methods for diagnosing shoulder pain in older adult patients in primary care. The shoulder's complex anatomy and its broad range of motion underscore the need for a systematic approach to evaluation. Routine inspection and palpation can identify signs such as muscle atrophy, bony protrusions, or indications of degenerative changes. Assessing range of motion, and distinguishing between active and passive deficits, is crucial for differentiating conditions like frozen shoulder from rotator cuff tears. Targeted strength tests, such as the empty can, external rotation lag, liftoff, and belly press tests, are instrumental in isolating specific rotator cuff muscles. Additionally, impingement tests, including Neer’s and Hawkins’ signs, are useful for detecting subacromial impingement. A comprehensive understanding of shoulder anatomy and a systematic physical examination are vital for accurately diagnosing shoulder pain in older adults. When properly executed and interpreted in the clinical context, these maneuvers help differentiate between various conditions, ranging from degenerative changes to rotator cuff pathology.

Shoulder diseases pose a significant health challenge for older adults, often causing pain, functional decline, and decreased independence. This narrative review explores how deep learning (DL) can address diagnostic challenges by automating tasks such as image segmentation, disease detection, and motion analysis. Recent research highlights the effectiveness of DL-based convolutional neural networks and machine learning frameworks in diagnosing various shoulder pathologies. Automated image analysis facilitates the accurate assessment of rotator cuff tear size, muscle degeneration, and fatty infiltration in MRI or CT scans, frequently matching or surpassing the accuracy of human experts. Convolutional neural network-based systems are also adept at classifying fractures and joint conditions, enabling the rapid identification of common causes of shoulder pain from plain radiographs. Furthermore, advanced techniques like pose estimation provide precise measurements of the shoulder joint's range of motion and support personalized rehabilitation plans. These automated approaches have also been successful in quantifying local osteoporosis, utilizing machine learning-derived indices to classify bone density status. DL has demonstrated significant potential to improve diagnostic accuracy, efficiency, and consistency in the management of shoulder diseases in older patients. Machine learning-based assessments of imaging data and motion parameters can help clinicians optimize treatment plans and improve patient outcomes. However, to ensure their generalizability, reproducibility, and effective integration into routine clinical workflows, large-scale, prospective validation studies are necessary. As data availability and computational resources increase, the ongoing development of DL-driven applications is expected to further advance and personalize musculoskeletal care, benefiting both healthcare providers and the aging population.