Assessing the Utilization of Large Language Models in Medical Education: Insights From Undergraduate Medical Students.

Background Artificial intelligence (AI) has the potential to be integrated into medical education. Among AI-based technology, large language models (LLMs) such as ChatGPT, Google Bard, Microsoft Bing, and Perplexity have emerged as powerful tools with capabilities in natural language processing. With this background, this study investigates the knowledge, attitude, and practice of undergraduate medical students regarding the utilization of LLMs in medical education in a medical college in Jharkhand, India. Methods A cross-sectional online survey was sent to 370 undergraduate medical students on Google Forms. The questionnaire comprised the following three domains: knowledge, attitude, and practice, each containing six questions. Cronbach's alphas for knowledge, attitude, and practice domains were 0.703, 0.707, and 0.809, respectively. Intraclass correlation coefficients for knowledge, attitude, and practice domains were 0.82, 0.87, and 0.78, respectively. The average scores in the three domains were compared using ANOVA. Results A total of 172 students participated in the study (response rate: 46.49%). The majority of the students (45.93%) rarely used the LLMs for their teaching-learning purposes (chi-square (3) = 41.44, p < 0.0001). The overall score of knowledge (3.21±0.55), attitude (3.47±0.54), and practice (3.26±0.61) were statistically significantly different (ANOVA F (2, 513) = 10.2, p < 0.0001), with the highest score in attitude and lowest in knowledge. Conclusion While there is a generally positive attitude toward the incorporation of LLMs in medical education, concerns about overreliance and potential inaccuracies are evident. LLMs offer the potential to enhance learning resources and provide accessible education, but their integration requires further planning. Further studies are required to explore the long-term impact of LLMs in diverse educational contexts.

Alcohol induced effects on kidney.

After administration ethanol and its metabolites go through kidneys and are excreted into urine, and its content in the urine is higher than that of the blood and the liver. Chronic ethanol administration decreases the renal tubular reabsorption and reduces renal function. Multiple functional abnormalities of renal tubules may be associated with ethanol-induced changes in membrane composition and lipid peroxidation. The vulnerability of the kidney to oxidative damage has been partly attributed to its high content of long-chain polyunsaturated fatty acids. Renal ultra structural abnormalities due to ethanol exposure may be important in the genesis of functional disturbances. Increased oxidative stress and endothelial dysfunction with their complex interrelationships are relevant aspects of atherogenesis in chronic renal failure. Antioxidants, particularly polyphenols are expected to decrease the vulnerability of the kidney to oxidative challenges.