Researchers are exploring various methods for antibiotic susceptibility testing, including minimum inhibitory concentration (MIC) assays and other techniques. These methods aim to accurately and rapidly detect antibiotic resistance in different types of bacteria, including those found in environmental samples. The development of new testing workflows and technologies, such as machine learning-assisted platforms, is also being investigated to improve the efficiency and accuracy of antibiotic susceptibility testing.