Yoğun Bakımda Yapay Zekâ Uygulamaları: Güncel Durum ve Yenilikçi Yaklaşımlar

Yıl 2025, Cilt: 34 Sayı: 4, 274 - 281, 24.12.2025

Kadir Kabahasanoğlu , Muammer Hayri Bektaş

https://doi.org/10.17827/aktd.1707456

Öz

Yoğun bakım ünitelerinde (YBÜ) yapay zekâ (YZ) ve makine öğrenimi tabanlı yaklaşımlar, yüksek hacimli çok modlu veriden klinisyenin gözden kaçırabileceği örüntüleri ortaya çıkarmaya olanak tanıyarak erken tanı, risk öngörüsü ve tedavi optimizasyonu için umut vaat etmektedir. Derin öğrenme algoritmaları akciğer grafisi, toraks-bilgisayarlı tomografi (BT) ve travma beyin BT’sinde infiltrat, ödem, pnömoni veya kanama gibi bulguları insan gözleminden daha hızlı ve objektif saptayabilmekte; gerçek zamanlı ultrason analizi gibi alanlara da genişleme potansiyeli göstermektedir. Sürekli vital bulgulara dayanan erken uyarı sistemleri, hipotansiyon, kardiyorespiratuvar instabilite veya sepsis gelişimini saatler öncesinden yüksek doğrulukla tahmin edebilmekte, hastane mortalitesi öngörüsünde geleneksel skorlama sistemlerini aşmaktadır. Kümelenme ve sınıflandırma yöntemleri, sepsis ve akut solunum sıkıntısı sendromu (ARDS) gibi heterojen sendromları biyolojik ve klinik alt fenotiplere ayırarak kişiselleştirilmiş tedavi olasılığını artırmıştır. Pekiştirmeli öğrenme ise sıvı-vazopresör yönetimi ve mekanik ventilatör ayarlarını dinamik olarak optimize eden “YZ-klinis¬yeni” modelleriyle hasta bazlı kararlara rehberlik edebilmektedir. Bununla birlikte, veri standardizasyonu ve paylaşımındaki yetersizlik, algoritmaların farklı merkezlere genellenebilirliğini sınırlamakta; gözlemsel verideki yanlılıklar ve “kara kutu” modellerin açıklanamazlığı klinik güveni zedelemektedir. Etik ve yasal çerçeveler, algoritmik adalet, sorumluluk paylaşımı ve hasta gizliliği konularında hâlen netleşmemiştir. Geleceğin odak noktaları; güvenli çok merkezli veri ekosistemleri, yorumlanabilir ve prospektif olarak doğrulanmış modeller, alarm yorgunluğunu azaltan akıllı arayüzler, dijital ikiz simülasyonları ve YZ okuryazarlığı yüksek sağlık profesyonelleridir. Sonuç olarak, YZ insan uzmanlığının yerini almayacak; ancak iyi tasarlandığında klinik karar süreçlerini tamamlayan, hızlı, özelleştirilmiş ve kanıta dayalı yoğun bakım hizmeti sunulmasına katkı sağlayacaktır.

Anahtar Kelimeler

Yoğun bakım , yapay zekâ , makine öğrenimi , klinik karar destek sistemleri , kişiselleştirilmiş tedavi

Artificial Intelligence Applications in Intensive Care: Current State and Innovative Perspectives

Öz

Artificial intelligence (AI) and machine learning techniques hold great promise for intensive care units (ICUs) by extracting clinically relevant patterns from high-volume, multimodal data that may elude human observation. Deep learning algorithms already outperform clinicians in the rapid, objective detection of infiltrates, oedema, pneumonia and haemorrhage on chest radiographs, thoracic computed tomography (CT) and trauma head CT, and are poised to extend to real-time ultrasound interpretation. Early warning systems that continuously analyse vital-sign streams can predict hypotension, cardiorespiratory instability and sepsis hours in advance, surpassing established scoring systems for predicting hospital mortality. Unsupervised clustering and classification have begun to dissect heterogeneous syndromes such as sepsis and acute respiratory distress syndrome (ARDS) into biologically and clinically distinct sub-phenotypes, paving the way for precision therapeutics. Reinforcement learning frameworks are emerging that dynamically optimise fluid–vasopressor management and ventilator settings, creating AI–clinician hybrids for patient-specific decision-making. Nevertheless, inadequate data standardisation and sharing restrict external validity, while biases in observational datasets and the opacity of “black-box” models erode clinical confidence. Ethical and legal frameworks remain unsettled regarding algorithmic fairness, accountability and patient privacy. Future priorities include secure multicentre data ecosystems, interpretable and prospectively validated models, smart user interfaces that mitigate alarm fatigue, digital-twin simulations and a workforce proficient in AI literacy. Ultimately, AI will not supplant human expertise but, when thoughtfully integrated, will augment clinical decision-making to deliver faster, more personalised and evidence-based critical-care services.

Anahtar Kelimeler

Intensive care , artificial intelligence , machine learning , clinical decision support systems , personalised therapy

Kaynakça

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