create a 50 slide presentation on the topic- AI AND MACHINE LEARNING IN MICROBIOLOGY. COVERING AI/ML BASICS, POTENTIAL OF AI IN MICROBIOLOGY(IN DIAGNOSIS, IPC, DATA MANAGEMENT AND SURVEILLANCE). USES OF AI IN MICROBIOLOGY(MICROSCOPY AND IMAGE ANALYSIS, CULTURE/COLONY MORPHOLOGY, MALDI TOF, AST AND AMR PREDICTIONS, MOLECULAR DIAGNOSTICS/ GENOMICS, INFECTION SURVEILLANCE/ OUTBREAK PREDICTIONS, CLINICAL DESIGN SUPPORT), APPLICATIONS AND PUBLISHED STUDIES, AI IN BACTERIOLOGY, PARASITOLOGY, VIROLOGY, MYCOLOGY. LIMITATIONS AND PITFALLS . FUTURE OF AI IN CLINICAL MICROBIOLOGY, TAKE HOME MESSAGE.
create a 50 slide presentation on the topic- AI AND MACHINE LEARNING IN MICROBIOLOGY. COVERING AI/ML BASICS, POTENTIAL OF AI IN MICROBIOLOGY(IN DIAGNOSIS, IPC, DATA MANAGEMENT AND SURVEILLANCE). USES OF AI IN MICROBIOLOGY(MICROSCOPY AND IMAGE ANALYSIS, CULTURE/COLONY MORPHOLOGY, MALDI TOF, AST AND AMR PREDICTIONS, MOLECULAR DIAGNOSTICS/ GENOMICS, INFECTION SURVEILLANCE/ OUTBREAK PREDICTIONS, CLINICAL DESIGN SUPPORT), APPLICATIONS AND PUBLISHED STUDIES, AI IN BACTERIOLOGY, PARASITOLOGY, VIROLOGY, MYCOLOGY. LIMITATIONS AND PITFALLS . FUTURE OF AI IN CLINICAL MICROBIOLOGY, TAKE HOME MESSAGE.
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This lecture introduces the applications of artificial intelligence and machine learning in clinical microbiology. It covers laboratory use cases, key AI approaches, supervised, unsupervised and self-supervised learning, and the importance of data quality, labelling and validation. Learners will examine how AI can address rising workloads, antimicrobial resistance and digital diagnostics, while identifying high-value decision points, preventing data leakage and planning safe, accountable...