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Global public health is seriously threatened by Antimicrobial Resistance (AMR), and there is an urgent need for quick and precise AMR diagnostic tools. The prevalence of novel Antibiotic Resistance Genes (ARGs) has increased substantially during the last decade, owing to the recent burden of microbial sequencing. The major problem is extracting vital information from the massive amounts of generated data. Even though there are many tools available to predict AMR, very few of them are accurate and can keep up with the unstoppable growth of data in the present. Here, we briefly examine a variety of AMR prediction tools that are available. We highlighted three potential tools from the perspective of the user experience that is preferable web-based AMR prediction analysis, as a web-based tool offers users accessibility across devices, device customization, system integration, eliminating the maintenance hassles, and provides enhanced flexibility and scalability. By using the Pseudomonas aeruginosa Complete Plasmid Sequence (CPS), we conducted a case study in which we identified the strengths and shortcomings of the system and empirically discussed its prediction efficacy of AMR sequences, ARGs, amount of information produced and visualisation. We discovered that ResFinder delivers a great amount of information regarding the ARGS along with improved visualisation. KmerResistance is useful for identifying resistance plasmids, obtaining information about related species and the template gene, as well as predicting ARGs. ResFinderFG does not provide any information about ARGs, but it predicts AMR determinants and has a better visualisation than KmerResistance.

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