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Non-subungual glomus tumor in the fingers is very rare. To explore the insights gained from diagnosing and treating non-subungual glomus tumors in the finger, we collected 19 cases in the last ten years, which were only reported as isolated cases in the previous literature. This was a retrospective study of 19 surgical patients between May 2012 and January 2022 with preoperative MRI, ultrasound, and their final pathological diagnosis is glomus tumor, while the lesions were located not under the nail bed. Immunohistochemistry indicated that SMA(+)100%, CD34(+)89.5%, Ki-67(+1∼3%)68.4%, CD31(vessel+)26.3%, Desmin(+)21%, Calponin(+) 10.5%, Vimentin(+)5.2%, Caldesmon(+)5.2%. The sensitivity of preoperative MRI was 100%, while the sensitivity of ultrasound was 63.2%. And the diagnosing accuracy rate of preoperative MRI was 36.8%. Preoperative MRI is helpful in the diagnosis of non-subungual glomus tumor in fingers. Ultrasound can be used as a supplemental examination technique, and the results depend on the operator and tumor location. Surgical excision is an effective therapy for non-subungual GT in the fingers.

The increasing integration of Artificial Intelligence (AI) in the medical domain signifies a transformative era in healthcare, with promises of improved diagnostics, treatment, and patient outcomes. However, this rapid technological progress brings a concomitant surge in ethical challenges permeating medical education. This paper explores the crucial role of medical educators in adapting to these changes, ensuring that ethical education remains a central and adaptable component of medical curricula. Medical educators must evolve alongside AI’s advancements, becoming stewards of ethical consciousness in an era where algorithms and data-driven decision-making play pivotal roles in patient care. The traditional paradigm of medical education, rooted in foundational ethical principles, must adapt to incorporate the complex ethical considerations introduced by AI. This pedagogical approach fosters dynamic engagement, cultivating a profound ethical awareness among students. It empowers them to critically assess the ethical implications of AI applications in healthcare, including issues related to data privacy, informed consent, algorithmic biases, and technology-mediated patient care. Moreover, the interdisciplinary nature of AI’s ethical challenges necessitates collaboration with fields such as computer science, data ethics, law, and social sciences to provide a holistic understanding of the ethical landscape.