RESUMEN
This study aimed to compare the efficacy of percutaneous microwave ablation therapy (MWAT) and laparoscopic partial nephrectomy (LPN) in early-stage renal cell carcinoma (RCC) classified as T1a; a retrospective analysis was conducted on patients treated between January 2017 and November 2023. Oncological outcomes, radiological recurrence, length of stay (LOS), and costs were evaluated. The study included 110 patients, with no significant differences between the two groups regarding residual tumors, local tumor progression, and disease-free survival rates (p > 0.05). The LPN group showed significantly lower pre/postoperative serum urea and creatinine and higher estimated glomerular filtration rate values, whereas the MWA group experienced significantly lower mean costs, complication rates, LOS in the hospital, and procedure durations (p ≤ 0.05). However, post-procedure residual tumors and local tumor progression rates did not differ significantly between the LPN and MWAT groups (p > 0.05). MWAT is as effective as LPN for T1a RCC lesions. In addition, MWAT has lower costs than LPN and is a cost-effective treatment method. Therefore, MWAT minimizes hospital stay and complications and since the oncological results are similar to LPN, it might be considered as the first choice of treatment in young patients.
RESUMEN
BACKGROUND: To examine the artificial intelligence (AI) tools currently being studied in modern medical education, and critically evaluate the level of validation and the quality of evidence presented in each individual study. METHODS: This review (PROSPERO ID: CRD42023410752) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. A database search was conducted using PubMed, Embase, and Cochrane Library. Articles written in the English language between 2000 and March 2023 were reviewed retrospectively using the MeSH Terms "AI" and "medical education" A total of 4642 potentially relevant studies were found. RESULTS: After a thorough screening process, 36 studies were included in the final analysis. These studies consisted of 26 quantitative studies and 10 studies investigated the development and validation of AI tools. When examining the results of studies in which Support vector machines (SVMs) were employed, it has demonstrated high accuracy in assessing students' experiences, diagnosing acute abdominal pain, classifying skilled and novice participants, and evaluating surgical training levels. Particularly in the comparison of surgical skill levels, it has achieved an accuracy rate of over 92%. CONCLUSION: AI tools demonstrated effectiveness in improving practical skills, diagnosing diseases, and evaluating student performance. However, further research with rigorous validation is required to identify the most effective AI tools for medical education.