Artificial intelligence in healthcare: historical development, terminology, concepts, and classifications

Background. Artificial intelligence (AI) plays a central role in contemporary medicine. The rapid increase in the number of developed approved software products, the deployment of clinical decision support systems, and the growing body of published research indicate that AI technologies are transitioning from the experimental stage to routine clinical use. However, the field still lacks unified terminology and a systematic classification of AI-based medical software.

Objective: To develop a coherent terminology and classification framework for AI in medicine; to compare major AI architectures; and to define several new concepts needed to describe contemporary AI software architectures.

Material and methods. A systematic search of PubMed/MEDLINE, Scopus, Web of Science, and regulatory databases (FDA, EMA) was conducted. Eligible sources included peer-reviewed publications and regulatory guidance documents in English and Russian (1950–2025). Terminology systematization and classification were performed using content analysis, and internationally established terms were adapted to Russian-language usage through expert consensus.

Results. The article examines the development of AI in clinical medicine and healthcare, tracing its progression from the expert systems of the 1970s to contemporary large language models and multimodal systems. It systematizes the major neural network architectures used in medical AI, including convolutional neural networks (CNN), vision transformers (ViT), hybrid CNN+ViT architectures, recurrent neural network (RNN) models, long short-term memory (LSTM) networks, and transformer-based large language model (LLM) (such as bidirectional encoder representations from transformer, BERT), and generative pre-trained transformer (GPT). This analysis provides a comprehensive classification of modern neural network architectures and AI systems according to modality, number of models, and deployment setting, and formulates practical recommendations to support method selection. In addition, the study systematizes and adapts the concepts of “inference”, “single-model/multi-model AI”, “computer vision program”, and recommends their standardized use in Russian-language medical terminology.

Conclusion. The development of a unified terminology and standardized approaches to the classification and validation of AI programs is essential for their safe and effective clinical application.

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