This paper summarizes the findings of many recent studies, specifically in the diagnosis of osteoporosis, osteoarthritis, and bone tumors. The paper is broken down into four sections, namely a general overview of AI and current medical advancements, a rundown of common diagnostic techniques and AI types, evaluation of deep learning in osteoporosis, osteoarthritis,and bone tumor diagnosis, and finally limitations of deep learning. The beginning two sections serve as vital background information for researchers seeking to understand AI and to gain a reference for diagnostic techniques such as CT, MRI, and X-Ray. The valuable portion of this paper in terms of orthopedic relevance is section 3. Significant findings in osteoporosis as well as osteoarthritis diagnosis include AI analysis of hip X-Ray imaging, CT scans, and MRI imaging for discovery of microscopic bone changes. CT and MRI are also used for bone tumor diagnosis due to the ability of AI to make more accurate predictions from smaller visible differences than many clinicians' differentials. Finally, this paper addresses the limitations in generalizability and research methods such as clinician trust.