RESUMO
Background and objective ChatGPT is an artificial intelligence (AI) language model that has been trained to process and respond to questions across a wide range of topics. It is also capable of solving problems in medical educational topics. However, the capability of ChatGPT to accurately answer first- and second-order knowledge questions in the field of microbiology has not been explored so far. Hence, in this study, we aimed to analyze the capability of ChatGPT in answering first- and second-order questions on the subject of microbiology. Materials and methods Based on the competency-based medical education (CBME) curriculum of the subject of microbiology, we prepared a set of first-order and second-order questions. For the total of eight modules in the CBME curriculum for microbiology, we prepared six first-order and six second-order knowledge questions according to the National Medical Commission-recommended CBME curriculum, amounting to a total of (8 x 12) 96 questions. The questions were checked for content validity by three expert microbiologists. These questions were used to converse with ChatGPT by a single user and responses were recorded for further analysis. The answers were scored by three microbiologists on a rating scale of 0-5. The average of three scores was taken as the final score for analysis. As the data were not normally distributed, we used a non-parametric statistical test. The overall scores were tested by a one-sample median test with hypothetical values of 4 and 5. The scores of answers to first-order and second-order questions were compared by the Mann-Whitney U test. Module-wise responses were tested by the Kruskall-Wallis test followed by the post hoc test for pairwise comparisons. Results The overall score of 96 answers was 4.04 ±0.37 (median: 4.17, Q1-Q3: 3.88-4.33) with the mean score of answers to first-order knowledge questions being 4.07 ±0.32 (median: 4.17, Q1-Q3: 4-4.33) and that of answers to second-order knowledge questions being 3.99 ±0.43 (median: 4, Q1-Q3: 3.67-4.33) (Mann-Whitney p=0.4). The score was significantly below the score of 5 (one-sample median test p<0.0001) and similar to 4 (one-sample median test p=0.09). Overall, there was a variation in median scores obtained in eight categories of topics in microbiology, indicating inconsistent performance in different topics. Conclusion The results of the study indicate that ChatGPT is capable of answering both first- and second-order knowledge questions related to the subject of microbiology. The model achieved an accuracy of approximately 80% and there was no difference between the model's capability of answering first-order questions and second-order knowledge questions. The findings of this study suggest that ChatGPT has the potential to be an effective tool for automated question-answering in the field of microbiology. However, continued improvement in the training and development of language models is necessary to enhance their performance and make them suitable for academic use.
RESUMO
Introduction The outbreak of Acinetobacter calcoaceticus baumannii ( ACB ) is mainly reported to be a notorious pathogens at health-care settings. It is the major problem on the health-care system with high morbidity and mortality rates because of the broad range of antibiotic resistance and lack of understanding the mechanism of developing new antibiotic resistance rapidly. It emphasizes the importance of local surveillance in describing or understanding and predicting microbial resistance patterns so that there will be limited use of antibiotics by developing strategies to control the extensive use of antimicrobial chemotherapy in clinical environment, which is still considered as one of the factors in the emergence of multidrug resistance microorganisms. Objectives The study aims to detect the occurrence rate of ACB infections from various clinical samples, identify the resistance levels to different groups of antimicrobial agents, and the occurrence rate of multidrug resistant (MDR) ACB clinical isolates from a tertiary hospital in Durgapur, West Bengal, India. Material and Methods The study was performed in the Department of Microbiology of the IQ City Medical College and Hospital, Durgapur, West Bengal, India, for the 24 months duration, that is, from January 1, 2018 to December 31, 2019. Altogether 15,800 clinical samples consisting of endotracheal tube aspirates, sputum, pus, blood, catheter tips, urine, tissue, and other body fluids were studied. ACB from clinical samples were identified by its characteristic colonies (nonlactose fermenting, glistening, small mucoid colonies), Gram-staining pattern (Gram-negative coccobacillus), and standard biochemical reactions. It was further confirmed in the Department of Microbiology of the Healthworld Hospital, Durgapur, West Bengal, India, by Vitek2 compact system (bioMerieux, Inc., Durham, North Carolina, United States). Antibiotic susceptibility testing was performed using automated broth microdilutions by Vitek2 compact system (bioMerieux, Inc.) and Kirby-Bauer disk diffusion test on Mueller-Hinton Agar (HiMedia). Results Nonrepetitive 289 ACB were isolated from various clinical samples. A total of 277 (96%) isolates of ACB were MDR strains. Conclusion ACB was mostly isolated from the intensive care unit department and was found to be the most MDR type in the tertiary care hospital by this study.
