The damage caused by the problem of drug-resistant bacteria is enormous, but clinical practice is reluctant to solve it.
The reason is the large amount of work of clinical laboratory technicians, which is the key to the proper use of antibacterial drugs, which is effective in solving the problem of drug-resistant bacteria. In this study, we study a macro environment using forecasting / backcasting methods(① introduction of an AI-based bacterial diagnosis assistance system into clinical laboratories and on-site hearings ② on-site surveys overseas where the problem of drug-resistant bacteria is becoming more serious) . In ①, multiple stakeholders are involved in 1) building a relationship of trust through continuous communication 2) assessing existing problem-solving methods, on-site needs, and listening to problems 3) organizing processes up to demonstration experiments and stable practical application. Conduct research and development for the purpose, conduct on-site feedback surveys ① ② Process construction in introducing AI to medical sites, consensus formation> development> demonstration experiments> assessment / improvement> practical application, stakeholders at each stage From the two perspectives of internal environment analysis such as problem analysis and effect verification that occur with the relationship of trust and introduction, ethical discussions and legal and moral norm issues regarding AI technology, and external environmental analysis such as macro-environmental understanding of drug-resistant bacteria. Organize and present research results that contribute to the sustainable development of the medical industry.