Integrative Artificial Intelligence in Regional Anesthesia: Enhancing Precision, Efficiency, Outcomes and Limitations

Year 2024, Volume: 5 Issue: 1, 52 - 66, 28.03.2024

Suna Kara Görmüş

https://doi.org/10.58770/joinihp.1435169

Abstract

Artificial intelligence (AI) has made remarkable progress in various domains, outperforming human capabilities in many areas. It is no surprise that AI is being increasingly used in healthcare practices, including regional anesthesia. Recent advancements in AI have enabled its integration into the field of regional anesthesia, promising to enhance precision, efficiency, and patient outcomes. By utilizing machine learning algorithms and predictive analytics, AI has the potential to revolutionize the way regional anesthesia procedures are conducted and managed. Ultrasound-guided regional anesthesia (UGRA) significantly enhances the success rates of regional blocks while mitigating complication risks. This review scrutinizes the burgeoning role of artificial intelligence (AI) in UGRA, detailing its evolution and pivotal function in optimizing sonographic imaging, target delineation, needle guidance, and local anesthetic administration. AI's support is invaluable, particularly for non-experts in training and clinical practice and for experts in educational settings. By systematically analyzing the capabilities and applications of AI in regional anesthesia, we assess its contribution to procedural precision, safety, and educational advancement. The findings reveal that AI-assisted UGRA not only bolsters the accuracy of anatomical identification, thus improving patient safety, but also standardizes the quality of care across varying expertise levels. The integration of AI into UGRA emerges as a transformative influence in anesthesiology, promising to reshape the domain with enhanced precision, efficiency, and patient-centered care.

Keywords

artificial ıntelligence, ultrasound-guided blocks, machine learning, predictive analytics, anesthesia safety, procedural precision, clinical decision support, aı in healthcare, anesthesia education

Rejyonel Anestezi Uygulamalarında Bütünleşik Yapay Zeka: Duyarlılığın, Verimliliğin, Sonuçların ve Sınırlamaların İyileştirilmesi

Abstract

Yapay zeka (YZ), birçok alanda insan yeteneklerini aşan olağanüstü ilerlemeler kaydetmiştir. YZ'nin, rejyonel anestezi de dahil olmak üzere sağlık uygulamalarında giderek daha fazla kullanılıyor olması şaşırtıcı değildir. Yapay zekadaki son gelişmeler, rejyonel anestezinin alanına entegrasyonunu mümkün kılmış ve hassasiyeti, verimliliği ve hasta sonuçlarını artırmayı vaat etmektedir. Makine öğrenimi algoritmaları ve tahmine dayalı analitikleri kullanarak, YZ'nin rejyonel anestezi prosedürlerinin yürütülme ve yönetilme biçimini devrim niteliğinde değiştirme potansiyeli bulunmaktadır. Ultrason eşliğinde yapılan rejyonel anestezi (UGRA), rejyonel blokların başarı oranlarını önemli ölçüde artırırken komplikasyon risklerini azaltmaktadır. Bu derleme, UGRA'daki Yapay Zeka'nın (YZ) artan rolünü detaylandırarak, sonografik görüntülemenin, hedef belirlemenin, iğne rehberliğinin ve lokal anestezik uygulamanın iyileştirilmesindeki kritik işlevini ve evrimini incelemektedir. Özellikle eğitimdeki uzman olmayanlar ve klinik uygulamada, ayrıca eğitim ortamlarındaki uzmanlar için YZ desteği paha biçilmezdir. Rejyonel anestezi alanındaki YZ'nin yeteneklerini ve uygulamalarını sistematik olarak analiz ederek, prosedürel hassasiyete, güvenliğe ve eğitsel ilerlemeye katkısını değerlendiriyoruz. Bulgular, YZ destekli UGRA'nın sadece anatomik tanımlamanın doğruluğunu artırarak hasta güvenliğini iyileştirmekle kalmayıp, aynı zamanda farklı uzmanlık seviyeleri arasında bakım kalitesini standartlaştırdığını ortaya koymaktadır. YZ'nin UGRA'ya entegrasyonu, artırılmış hassasiyet, verimlilik ve hasta odaklı bakım ile anestezi alanını dönüştürme vaadi taşıyan bir etki olarak ortaya çıkmaktadır.

Keywords

Rejyonel anestezi, yapay zeka, ultrason eşliğinde bloklar, makine öğrenimi, tahmine dayalı analitik

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