Medical student’s attitudes and perceptions toward artificial intelligence applications

To evaluate medical students' perceptions in radiology and medical applications of artificial intelligence (AI). Students at 10 prestigious medical schools were issued an online survey that was created using Survey Monkey. It was divided into many parts with the goal of assessing the students' past understanding of AI in radiology and beyond as well as their attitudes about AI in medicine more generally. Anonymity of the respondents was protected. A total of 263 students—166 female and 94 male—with a median age of 23—replied to the survey. Concerning 52 percent of respondents were aware of the current debate about AI in radiology, while 68 percent said they were ignorant of the underlying technology. abnormalities in radiological scans, but they believed that AI would not be able to provide a definitive diagnosis (56 percent). In contrast to claims that human radiologists would be displaced, the majority (77 percent and 86 percent) believed that AI would revolutionize and enhance radiology (83 percent). Over two-thirds of respondents felt that medical education must include AI (71 percent). Male and tech-savvy respondents had higher levels of confidence in the advantages of AI and lower levels of fear of these technologies in sub-group analyses. In conclusion, Contrary to what has been mentioned in the media, medical students are aware of the possible applications and effects of AI on radiology and medicine and do not worry that it will replace human radiologists. The situations in which artificial intelligence has reportedly substituted human radiologists are known to medical students. Since it is their duty, the field of radiology must take the initiative in teaching students about these freshly developed tools

Medical Student’s Attitudes and Perceptions Toward Artificial Intelligence Applications

To evaluate medical students' perceptions in radiology and medical applications of artificial intelligence (AI). Students at 10 prestigious medical schools were issued an online survey that was created using Survey Monkey. It was divided into many parts with the goal of assessing the students' past understanding of AI in radiology and beyond as well as their attitudes about AI in medicine more generally. Anonymity of the respondents was protected. A total of 263 students—166 female and 94 male—with a median age of 23—replied to the survey. Concerning 52 percent of respondents were aware of the current debate about AI in radiology, while 68 percent said they were ignorant of the underlying technology. abnormalities in radiological scans, but they believed that AI would not be able to provide a definitive diagnosis (56 percent). In contrast to claims that human radiologists would be displaced, the majority (77 percent and 86 percent) believed that AI would revolutionize and enhance radiology (83 percent). Over two-thirds of respondents felt that medical education must include AI (71 percent). Male and tech-savvy respondents had higher levels of confidence in the advantages of AI and lower levels of fear of these technologies in sub-group analyses. In conclusion, Contrary to what has been mentioned in the media, medical students are aware of the possible applications and effects of AI on radiology and medicine and do not worry that it will replace human radiologists. The situations in which artificial intelligence has reportedly substituted human radiologists are known to medical students. Since it is their duty, the field of radiology must take the initiative in teaching students about these freshly developed tools

Risk and diagnostic factors and therapy outcome of neonatal early onset sepsis in ICU patients of Saudi Arabia: a systematic review and meta analysis

BackgroundNeonatal early onset sepsis (NEOS) is a serious and potentially life-threatening condition affecting newborns within the first few days of life. While the diagnosis of NEOS was based on clinical signs and symptoms in the past, recent years have seen growing interest in identifying specific diagnostic factors and optimizing therapy outcomes. This study aims to investigate the diagnostic and risk factors and therapy outcomes of neonatal EOS in ICU patients in Saudi Arabia, with the goal of improving the management of neonatal EOS in the country.MethodsThis method outlines the protocol development, search strategy, study selection, and data collection process for a systematic review on neonatal early onset sepsis in Saudi Arabian ICU patients, following the PRISMA 2020 guidelines. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is a well-established guideline that provides a framework for conducting systematic reviews and meta-analyses in a transparent and standardized manner. It aims to improve the quality and reporting of such research by ensuring clear and comprehensive reporting of study methods, results, and interpretations. The search strategy included electronic databases (PubMed, Embase, Google Scholar, Science Direct, and the Cochrane Library) and manual search of relevant studies, and data were extracted using a standardized form.ResultsThe systematic review included 21 studies on neonatal sepsis in Saudi Arabia, with varying study designs, sample sizes, and prevalence rates of sepsis. Group B streptococcus and E. coli were the most commonly isolated pathogens. Various diagnostic factors and risk factors were reported, including hematological parameters, biomarkers, and blood cultures. The quality of the included studies was assessed using the Newcastle-Ottawa Scale and Joanna Briggs Institute critical checklist.ConclusionsThe review identified a number of risk and diagnostic factors and therapy outcomes for neonatal sepsis. However, most of the studies were having small scale cohort groups. Further research with controlled study designs is needed to develop effective prevention and management strategies for neonatal sepsis in Saudi Arabia