THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN ANESTHESIOLOGY AND REANIMATOLOGY: NEW OPPORTUNITIES, SCIENTIFIC EVIDENCE, AND FUTURE CHALLENGES

dc.contributor.authorUrunov Zokirjon Burxanovich
dc.date.accessioned2025-12-28T19:28:33Z
dc.date.issued2025-11-27
dc.description.abstractArtificial intelligence (AI) technologies are rapidly advancing in medicine, particularly in anesthesiology and reanimatology. This article provides a detailed examination of AI's role in real-time patient monitoring, individualized drug dosing, prediction of surgical outcomes, and decision-making in intensive care units. The analysis is grounded in international standards, such as those from the World Health Organization (WHO) and the American Society of Anesthesiologists (ASA). It incorporates findings from scientific studies published between 2023 and 2025, highlighting AI's potential to enhance patient safety and its associated risks. In compliance with the requirements of the Higher Attestation Commission of Uzbekistan (OAK), the article is based on original data with practical relevance. Results indicate that AI can reduce postoperative complications by 20–30%, but ethical, technical, and regulatory challenges must be addressed.
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dc.identifier.urihttps://ejird.journalspark.org/index.php/ejird/article/view/1734
dc.identifier.urihttps://asianeducationindex.com/handle/123456789/12788
dc.language.isoeng
dc.publisherJournal Park Publishing
dc.relationhttps://ejird.journalspark.org/index.php/ejird/article/view/1734/1667
dc.rightshttps://creativecommons.org/licenses/by-nd/4.0
dc.sourceEuropean Journal of Interdisciplinary Research and Development ; Vol. 45 (2025); 299-301
dc.source2720-5746
dc.subjectArtificial intelligence, anesthesiology, reanimatology, machine learning, monitoring, predictive modeling, ethical issues.
dc.titleTHE APPLICATION OF ARTIFICIAL INTELLIGENCE IN ANESTHESIOLOGY AND REANIMATOLOGY: NEW OPPORTUNITIES, SCIENTIFIC EVIDENCE, AND FUTURE CHALLENGES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typePeer-reviewed Article

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