METHOD: We searched relevant electronic databases, including PUBMED, MEDLINE, and SCOPUS, and performed a systematic review. Keywords used were "necrotizing fasciitis" or "necrotising fasciitis" or "necrotizing soft tissue infections" and "point-of-care ultrasonography" "ultrasonography" or "ultrasound". No temporal limitation was set. An additional search was performed via google scholar, and the top 100 entry was screened.
RESULTS: Among 540 papers screened, only 21 were related to diagnosing necrotizing fasciitis using ultrasonography. The outcome includes three observational studies, 16 case reports, and two case series, covering the period from 1976 to 2022.
CONCLUSION: Although the use of ultrasonography in diagnosing NF was published in several papers with promising results, more studies are required to investigate its diagnostic accuracy and potential to reduce time delay before surgical intervention, morbidity, and mortality.
METHOD: We conducted a cross-sectional analysis to compare ChatGPT, Google Bard, and medical students in mass casualty incident (MCI) triage using the Simple Triage And Rapid Treatment (START) method. A validated questionnaire with 15 diverse MCI scenarios was used to assess triage accuracy and content analysis in four categories: "Walking wounded," "Respiration," "Perfusion," and "Mental Status." Statistical analysis compared the results.
RESULT: Google Bard demonstrated a notably higher accuracy of 60%, while ChatGPT achieved an accuracy of 26.67% (p = 0.002). Comparatively, medical students performed at an accuracy rate of 64.3% in a previous study. However, there was no significant difference observed between Google Bard and medical students (p = 0.211). Qualitative content analysis of 'walking-wounded', 'respiration', 'perfusion', and 'mental status' indicated that Google Bard outperformed ChatGPT.
CONCLUSION: Google Bard was found to be superior to ChatGPT in correctly performing mass casualty incident triage. Google Bard achieved an accuracy of 60%, while chatGPT only achieved an accuracy of 26.67%. This difference was statistically significant (p = 0.002).