Formulating tasks, interpretation, and planning the implementation of research results using artificial intelligence in medicine.

Abstract

Strategic issues of artificial intelligence use in medicine are considered. Summarizing, as of today, AI supports doctors but does not replace them. It is emphasized that AI in healthcare typically solves important, but rather limited in scope, tasks. Difficulties in further implementation of AI are analyzed. The aim of the study was to address the analytical generalization of AI capabilities in healthcare, analyze the problems of using the Universum of medical-biological knowledge as a global unified resource, and conceptually justify the need to structure medical-biological knowledge, introducing fundamentally new forms of knowledge transfer in healthcare. Conclusions made: 1. The goal of AI implementation should be to find a delicate, mutually beneficial balance between its effective use and the judgments of trained doctors. This is extremely important, as artificial intelligence, which may practically fully replace the labour of doctors in the near future, today is an issue that might otherwise hinder obtaining benefits from it. 2. AI will become an integral part of future medicine. Therefore, it is important to teach the new generation of medical interns the concepts and principles of AI application, to function effectively in the workplace. It is extremely important to develop skills such as empathy in AI. 3. A systematic approach to the continuous improvement of diagnostic and treatment processes and systems for patients, first and foremost, requires bridging the gap between accumulated medical knowledge and the logic and results of AI use.

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