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Background: Ulnar neuropathy at the elbow is the second most common upper extremity compressive neuropathy and surgical treatment often involves surgical trainee involvement. The primary aim of this study is to determine the effect of trainees and surgical assistants on outcomes surrounding cubital tunnel surgery.

Methods: This retrospective study included 274 patients with cubital tunnel syndrome who underwent primary cubital tunnel surgery at two academic medical centres between 1 June 2015 and 1 March 2020. The patients were divided into four main cohorts based on primary surgical assistant: physician associates (PA, n = 38), orthopaedic or plastic surgery residents (n = 91), hand surgery fellows (n = 132), or both residents and fellows (n = 13). Exclusion criteria included patient age <18 years, revision surgery as the index procedure, prior traumatic ulnar nerve injury and concurrent procedures not related to cubital tunnel surgery. Demographics, clinical variables and perioperative findings were collected through chart reviews. Univariate and bivariate analyses were performed, and p < 0.05 was considered significant.

Results: Patients in all cohorts had similar demographic and clinical characteristics. There was a significantly higher rate of subcutaneous transposition in the PA cohort (39.5% PA vs. 13.2% Resident vs. 19.7% Fellow vs. 15.4% Resident + Fellow). Presence of surgical assistants and trainees had no association with length of surgery, complications and reoperation rates. Although male sex and ulnar nerve transposition were associated with longer operative times, no explanatory variables were associated with complications or reoperation rates.

Conclusions: Surgical trainee involvement in cubital tunnel surgery is safe and has no effect on operative time, complications or reoperation rates. Understanding the role of trainees and measuring the effect of graduated responsibility in surgery is important for medical training and safe patient care.

Level of Evidence: Level III (Therapeutic)

3D printing applications help solve challenges in the field of healthcare. These technologies evolved to produce custom-made medical devices and implants for patients and enhance medical education and research. This paper aims to make readers aware of the role of 3D printing in the field of medical education. 3D printing technologies are part of additive manufacturing (AM) technologies. 3D printing shows excellent potential with unconventional materials like different types of plastic, ketones, wood, human cells, metal powder, ceramics, composites, smart material, etc. This manufacturing method is suitable for producing complex and intricate shaped medical objects of the required property with lesser wastage of material. This paper introduces 3D printing technology and the need to carry out this study related to medical education and research. A brief literature review of 3D printing has been carried out. The paper further discusses the capabilities of 3D printing in the field of medicine. Patient-specific 3D models are being designed and then manufactured and implanted. 3D models of defective body parts help surgical planning and better part designing. Finally, the paper discusses significant roles of 3D printing in healthcare education in a tabular form. For the future, this technology has immense potential for medical education, surgical planning and support including for a clear understanding of the disease.

In the present study, the important role of biomedical engineering (BME) professions in medical education (ME) is highlighted. We propose that BME and ME should be integrated in a joint department to provide high-quality occupational education in medicine. Then, we illustrate the basic connections and close relations between BME and ME with respect to the universal subfields of each scientific branch. As this regard, the proposed joint department in medical faculty would guide the medicine to use innovative educational tools such as humanistic models, realistic simulations, video games, web-based online resources, etc. in both basic and clinical ME. In addition, the combination of two disciplines would prepare the initiative facilities for multidisciplinary original research studies improving human health. Moreover, new trends in instructional evaluation could be captured by studying together in teams.

The United States Medical Licensing Examination (USMLE) is a critical step in assessing the competence of future physicians, yet the process of creating exam questions and study materials is both time-consuming and costly. While Large Language Models (LLMs), such as OpenAI’s GPT-4, have demonstrated proficiency in answering medical exam questions, their potential in generating such questions remains underexplored. This study presents QUEST-AI, a novel system that utilizes LLMs to (1) generate USMLE-style questions, (2) identify and flag incorrect questions, and (3) correct errors in the flagged questions. We evaluated this system’s output by constructing a test set of 50 LLM-generated questions mixed with 50 human-generated questions and conducting a two-part assessment with three physicians and two medical students. The assessors attempted to distinguish between LLM and human-generated questions and evaluated the validity of the LLM-generated content. A majority of exam questions generated by QUEST-AI were deemed valid by a panel of three clinicians, with strong correlations between performance on LLM-generated and human-generated questions. This pioneering application of LLMs in medical education could significantly increase the ease and efficiency of developing USMLE-style medical exam content, offering a cost-effective and accessible alternative for exam preparation.