TRANSFORMATIONAL POTENTIAL OF MEDICAL ARTIFICIAL INTELLIGENCE FOR HEALTHCARE AND PROFESSIONAL EDUCATION: TECHNOLOGICAL COMPONENTS
DOI:
https://doi.org/10.11603/mie.1996-1960.2024.3-4.15460Keywords:
medical artificial intelligence, machine learning, deep learning, large multimodal models, natural language processing, computer vision, expert systems, intelligent roboticsAbstract
Background. Artificial intelligence (AI) is rapidly becoming a fundamental component of modern medicine, unlocking new opportunities for the diagnosis and treatment of patients. AI encompasses a broad range of methods, algorithms, and technologies aimed at creating computer systems capable of performing tasks traditionally requiring human intelligence. Medical AI, as a specialized subfield of AI, focuses on the application of AI technologies in healthcare. By analyzing large volumes of diverse data, uncovering hidden patterns, and generating accurate predictions, medical AI has the potential to fundamentally transform the healthcare paradigm, making it more precise, personalized, and efficient.
Materials and Methods. The research results were systematized using databases of scientific periodicals such as PubMed, Scopus, Web of Science, EMBASE, ERIC, IEEE Xplore, Semantic Scholar, ScienceDirect, and others. At various stages of the study, methods of bibliographic search, information structuring, and systematic analysis were employed.
Results. It is argued that the foundational technological components of medical artificial intelligence include machine learning (ML) and deep learning (DL) algorithms, large multimodal models (LMM), natural language processing (NLP), computer vision (CV), expert systems, and intelligent robotics.
These technologies enable the comprehensive analysis of diverse medical data, such as medical images, electronic health records (EHR), data from wearable devices and the Internet of Medical Things (IoMT), as well as genomic and proteomic information, to support clinical decision-making while incorporating evidence-based medical guidelines. The article thoroughly explores the foundations of ML and DL, providing a detailed description of their algorithms. It explains the concepts of generative AI and large multimodal models (LMM).
Conclusions. An essential task is to develop healthcare professionals’ competencies in utilizing medical AI, as a lack of understanding of its fundamental concepts and insufficient practical skills complicate its integration into clinical practice. The comprehensive analysis of modern medical AI technologies presented in this article not only enhances understanding of its potential but also outlines pathways for its implementation in clinical practice and professional education, laying the groundwork for further research.
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