Hybrid CNN-Transformer Approach to Bacterial Taxonomy in Medical Imaging

This article demonstrates Artificial Intelligence (AI) methods for\u2002characterization of bacteria, describes some of the challenges of utilizing these technologies, and coheres new possibilities for future research. Artificial intelligence (AI) is a fast-paced interdisciplinary field of science, dealing with\u2002multiple subfields, which focuses on creating machines capable of carrying out activities that require human intelligence . We\u2002consider the role of clinical diagnostics, environmental monitoring, and scientific discovery in characterizing bacterial isolates and the challenges of data quality, generalization, interpretability, resource requirements, ethics, domain adaptation, human\u2013AI collaboration, long-term maintenance, and fairness and accountability. These results indicate that\u2002AI models performed with an average accuracy of 92.5% with a diverse dataset of gram-stained bacterial images with accuracy varying between 85% and 98% depending on taxonomic level. The model was also\u2002resistant to different methods of staining. Improving the speed and accuracy of bacterial identification, this article addresses Sustainable Development Goal 3\u2002(Good Health and Well-being) by advancing clinical diagnostics and infection control. Finally, we call\u2002on the next generation to drive forwards interpretable AI, global cooperation and ethical AI.