Bidenko Natalia V, Stuchynska Nataliia V, Palamarchuk Yurii V, Matviienko Mykola M
BOGOMOLETS NATIONAL MEDICAL UNIVERSITY, KYIV, UKRAINE.
Wiad Lek. 2025;78(5):1199-1205. doi: 10.36740/WLek/205397.
Aim: To highlight the features of artificial intelligence application in healthcare, with an emphasis on specific AI solutions and the assessment of risks associated with such integration in ethical and regulatory dimensions.
Materials and Methods: To achieve the research objective, general scientific theoretical and empirical methods were used, including: bibliosemantic method - analysis of scientific, methodological, psychological, pedagogical literature, and regulatory documents on the research problem, system analysis method - to compare and generalize the experience of using artificial intelligence in healthcare, empirical methods - conversations and interviews with participants in the educational process, modeling - to implement a scheme for providing medical care using AI.
Conclusions: Generative artificial intelligence is rapidly developing and is already being used in healthcare. The resources discussed that utilizing artificial intelligence can be used by practicing doctors, patients, as well as higher education students and academic staff in the educational process for examining various clinical cases, better understanding the material, and accessing visualization databases. Therefore, the need to integrate AI technologies into the training process of healthcare professionals at higher medical educational institutions is evident. An important part of the research is addressing the key challenges that arise when applying AI in medicine: ethical and regulatory issues, as well as the difficulties in integrating with existing medical information systems. Further research should be aimed at developing clear recommendations for medical institutions and educational establishments regarding the implementation and use of AI technologies.
旨在突出人工智能在医疗保健领域应用的特点，重点关注特定的人工智能解决方案以及在伦理和监管层面评估与此类整合相关的风险。
材料与方法：为实现研究目标，采用了一般科学理论和实证方法，包括：文献语义法——分析关于研究问题的科学、方法学、心理学、教育学文献以及监管文件；系统分析法——比较和归纳在医疗保健领域使用人工智能的经验；实证方法——与教育过程中的参与者进行对话和访谈；建模——实施使用人工智能提供医疗服务的方案。
结论：生成式人工智能正在迅速发展，并且已经在医疗保健领域得到应用。所讨论的资源表明，执业医生、患者以及高等教育学生和学术人员在教育过程中可以利用人工智能来检查各种临床病例、更好地理解相关内容并访问可视化数据库。因此，将人工智能技术整合到高等医学教育机构医疗保健专业人员的培训过程中的必要性显而易见。该研究的一个重要部分是应对在医学中应用人工智能时出现的关键挑战：伦理和监管问题，以及与现有医疗信息系统整合的困难。进一步的研究应旨在为医疗机构和教育机构制定关于人工智能技术实施和使用的明确建议。
