RESUMO
Background Artificial intelligence (AI) has taken on a variety of functions in the medical field, and research has proven that it can address complicated issues in various applications. It is unknown whether Lebanese medical students and residents have a detailed understanding of this concept, and little is known about their attitudes toward AI. Aim This study fills a critical gap by revealing the knowledge and attitude of Lebanese medical students toward AI. Methods A multi-centric survey targeting 365 medical students from seven medical schools across Lebanon was conducted to assess their knowledge of and attitudes toward AI in medicine. The survey consists of five sections: the first part includes socio-demographic variables, while the second comprises the 'Medical Artificial Intelligence Readiness Scale' for medical students. The third part focuses on attitudes toward AI in medicine, the fourth assesses understanding of deep learning, and the fifth targets considerations of radiology as a specialization. Results There is a notable awareness of AI among students who are eager to learn about it. Despite this interest, there exists a gap in knowledge regarding deep learning, albeit alongside a positive attitude towards it. Students who are more open to embracing AI technology tend to have a better understanding of AI concepts (p=0.001). Additionally, a higher percentage of students from Mount Lebanon (71.6%) showed an inclination towards using AI compared to Beirut (63.2%) (p=0.03). Noteworthy are the Lebanese University and Saint Joseph University, where the highest proportions of students are willing to integrate AI into the medical field (79.4% and 76.7%, respectively; p=0.001). Conclusion It was concluded that most Lebanese medical students might not necessarily comprehend the core technological ideas of AI and deep learning. This lack of understanding was evident from the substantial amount of misinformation among the students. Consequently, there appears to be a significant demand for the inclusion of AI technologies in Lebanese medical school courses.
RESUMO
Robot-assisted Heller myotomy (RAHM) is an increasingly popular alternative to the traditional laparoscopic Heller myotomy (LHM) in the surgical management of achalasia, with similar outcomes and potentially lower complication rates. We aimed to systematically review the literature by comparing the technical success, outcomes, and complications of RAHM and LHM. We searched PubMed, Medline, and Cochrane Central Register for articles published between 2001 and 2023. Data on technical success, clinical outcomes, length of hospital stay, esophageal perforation rate, and overall mortality were extracted. A total of 11 articles were included in the study, comparing a total of 3,543 RAHM and 15,434 LHM cases. The mean operative time was significantly higher in the RAHM procedure with a total mean difference of 23.95 (95% confidence interval (Cl) 17.09, 30.81; p < 0.00001; I2 = 99%). However, the RAHM was associated with a significantly shorter hospital stay, with a total mean difference of -0.24 (95% Cl = -0.40, -0.08; p < 0.00001; I2 = 81%). The volume of blood loss was significantly smaller in RAHM with a total mean difference of -61.11 (95% CI = -150.31, 28.09; p < 0.00001; I2 = 99%). Esophageal mucosal perforation was significantly lower in RAHM with an odds ratio of 0.36 (95% CI = 0.16, 0.82; p = 0.02; I2 = 22%). Both procedures were associated with similar rates of symptom relief. Although no mortality was recorded in patients who underwent RAHM as opposed to 16 cases in patients who underwent LHM, no statistically significant difference could be reached. Our results demonstrate that while both procedures yield comparable clinical outcomes, RAHM is associated with a lower overall complication rate, particularly a lower rate of esophageal mucosal perforation, shorter hospital stay, and possibly a lower mortality rate. This confirms that RAHM is a viable and justifiable alternative to the conventional LHM in the surgical management of achalasia.
RESUMO
Laparoscopic sleeve gastrectomy (LSG) is the most widely performed bariatric surgery and has been associated with excellent outcomes and a significant reduction in obesity-related morbidity and mortality. Traditionally, this surgery is performed using five to seven trocars. However, LSG performed through a single trocar is emerging as a less invasive method of performing this surgery. This systematic review and meta-analysis compare the outcomes and complication rates of single-port versus multi-port LSG. We searched PubMed, Medline, Scopus, and the Cochrane Library for articles published from 2008 to 2023, in accordance with the PRISMA 2020 guidelines. Data on variables such as operative time, excess weight loss, intraoperative bleeding, postoperative leak, and incisional hernia rates were collected and analyzed using a random-effects model. Fourteen articles met the inclusion criteria and were included in the meta-analysis. No significant differences were found between the single-port LSG (SILSG) and conventional LSG (CLSG) groups in terms of operative time, rate, intraoperative complications, length of hospital stay, postoperative complications, and excess weight loss (EWL). Furthermore, single incision sleeve gastrectomy showed better satisfaction with the cosmetic score. SILSG is a viable alternative procedure, showing comparable outcomes to multiport conventional sleeve gastrectomy, in addition, to a better cosmetic satisfaction score.
