Medical digital twins, which represent medical assets, play a crucial role in connecting the physical world to the metaverse, enabling patients to access virtual medical services and experience immersive interactions with the real world. One serious disease that can be diagnosed and treated using this technology is cancer. However, the digitalization of such diseases for use in the metaverse is a highly complex process. To address this, this study aims to use machine learning (ML) techniques to create real-time and reliable digital twins of cancer for diagnostic and therapeutic purposes. The study focuses on four classical ML techniques that are simple and fast for medical specialists without extensive Artificial Intelligence (AI) knowledge, and meet the requirements of the Internet of Medical Things (IoMT) in terms of latency and cost. The case study focuses on breast cancer (BC), the second most prevalent form of cancer worldwide. The study also presents a comprehensive conceptual framework to illustrate the process of creating digital twins of cancer, and demonstrates the feasibility and reliability of these digital twins in monitoring, diagnosing, and predicting medical parameters.

The use of antiangiogenic and antiresorptive medications, particularly in patients with cancer or osteoporosis, can lead to osteonecrosis of the jaw following tooth extraction, trauma or arising spontaneously- A condition known as medication-related osteonecrosis of the jaw (MRONJ). In this article, we present a unique case of MRONJ in a patient with no history of antiresorptive or antiangiogenic drug use, who was instead taking the anti-interleukin 17-A (Secukinumab) medication for severe psoriasis. This association has not been previously reported in the literature.