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Introduction: As Artificial Intelligence (AI) technology continues to assimilate into various industries, there is a huge scope in the healthcare industry specifically in clinical laboratories. The perspective of the laboratory professionals can give valuable insight on the ideal path to take for AI implementation. Methods: The study utilized a cross-sectional survey design and was conducted at the section of Chemical Pathology, Department of Pathology and Laboratory Medicine, the Aga Khan University (AKU), Karachi, Pakistan in collaboration with Consultant Pathologists of 9 clinical laboratories associated with teaching hospitals across Pakistan from October-November 2023. The survey was for a duration of 2 weeks and was circulated to all working laboratory technical staff after informed consent. Results: A total of 351 responses were received, of which 342 (male=146, female=196) responses were recorded after exclusion. Respondents ranged from technologists, faculty, residents, and coordinators, and were from different sections (chemical pathology, microbiology, haematology, histopathology, POCT). Out of the total 312 (91.2%) of respondents stated that they were at least somewhat familiar with AI technology. Experts in AI were only 2.0% (n=7) of all respondents, but 90% (n=6) of these were < 30 years old. 76.3% (n=261) of the respondents felt the need to implement more AI technology in the laboratories, with time saving (26.1%) and improving performances of tests (17.7%) cited to be the greatest benefits of AI. Security concerns (n=144) and a fear of decreasing personal touch (n=143) were the main concerns of the respondents while the younger employees had an increased fear of losing their jobs. 76.3% were in favour of an increase in AI usage in the laboratories. Conclusion: This study highlights a favourable perspective among laboratory professionals, acknowledging the potential of AI to enhance both the efficiency and quality of laboratory practices. However, it underscores the importance of addressing their concerns in the thoughtful implementation of this emerging technology.
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PURPOSE: To determine the difference in antimicrobial susceptibility of various antibiotics using the CLSI & EUCAST breakpoints. METHODS: In this non interventional, retrospective observational study, we reviewed minimum inhibitory concentrations (MIC) of various antibiotics routinely reported for Enterobacteriaceae clinical isolates, from an automated microbiology identification system (VITEK-2). These MICs were then analysed using both CLSI 2019 and EUCAST 2019 guidelines and classified as per the breakpoints into various categories. RESULTS: The concordance rates of the antimicrobial susceptibility for various drugs ranged from 78.2% to 100% among two breakpoints. Perfect agreement with κâ¯=â¯1 (pâ¯<â¯0.001) was observed for only three antimicrobials ceftriaxone, levofloxacin and trimethoprim-sulfamethoxazole. The changes in antimicrobial susceptibility interpretation for cefepime, ciprofloxacin, amoxicillin clavulanic acid was majorly in Intermediate category. CONCLUSION: The change in interpretation of the susceptibility will lead to change in the usage of antibiotics especially due to recent change in definition of I by EUCAST. There is need of more studies in this aspect to ascertain clinical implication of change in antimicrobial susceptibility.
