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Journals / Hitit Medical Journal / 2026 / Cilt: 7 Sayı: 1

Will Machine Learning Take a Leading Role in Emergency Medicine Applications?

Acil Tıp Uygulamalarında Makine Öğrenimleri Başrolü Alır mı?

Savaş Sezik(İZMİR ÖDEMİŞ DEVLET HASTANESİ)0000-0002-0870-1050
Pages
118–123
Publication Date
2025-02-25
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

It is brought to the fore that machine learning models enhance clinicians' decision-making abilities, reduce diagnostic errors, and alleviate cognitive load

📌 3 Key Takeaways

🎯
Objective & Hypothesis:'The main problems in the workflows of emergency services can be summarised as over-crowding,unnecessary usage trends and long waiting times.
📊
Key Findings & Data:In the future, artifical intelligence-based machine learning models will be integrated to the clinical decision making support systems to reduce the workload of physicians, and also play an auxiliary role in the emergency services.
💡
Conclusion & Impact:It is brought to the fore that machine learning models enhance clinicians' decision-making abilities, reduce diagnostic errors, and alleviate cognitive load
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Acil Servis · Yapay Zeka · ChatGPT

Abstract

'The main problems in the workflows of emergency services can be summarised as over-crowding,unnecessary usage trends and long waiting times. Emergency services experienced a breaking point during the pandemic, and new approaches regarding to management have come to the agenda. Health care providers around the world are looking to artificial intelligence as the solution to these challenges. applications into emergency department business processes. In the future, artifical intelligence-based machine learning models will be integrated to the clinical decision making support systems to reduce the workload of physicians, and also play an auxiliary role in the emergency services. In this article, we will discuss on the basis of the reasons that led to the combination of machine learning to summarize the current status of modeling in emergency services. It is brought to the fore that machine learning models enhance clinicians' decision-making abilities, reduce diagnostic errors, and alleviate cognitive load

Özet

Acil servislerde iş akışlarındaki temel problemler; yoğunluk, gereksiz kullanım eğilimleri ve uzun bekleme süreleri olarak özetlenebilir. Covid-19 pandemisi sırasında kırılma noktasını yaşayan acil servis yönetiminde yeni yaklaşımlar gündeme gelmiştir. Sağlık hizmeti sağlayıcıları, dünya çapında bu zorlukların çözümü olarak, yapay zekâ uygulamalarını acil servis iş süreçlerine dâhil etmeye başlamışlardır. Yapay zeka tabanlı makine öğrenimi modelleri, gelecekte klinik karar destek sistemlerine entegre edilerek hekimlerin iş yükünü azaltmalarının yanında acil servis işleyişleri için de yardımcı rol oynayacaklar gibi görünmektedir. Biz bu yazımızda, acil serviste makine öğrenimi birlikteliğine götüren nedenler temelinde modellemelerin acil servis hizmetlerindeki güncel durumu özetlemeye çalıştık. Makine öğrenimi modellerinin klinisyenlerin karar verme yetilerini geliştirdiği, tanısal hataları ve bilişsel yüklenmeyi azalttığı görüşleri öne çıkmaktadır.

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Cite This Article

Sezik, S. (2025). Will Machine Learning Take a Leading Role in Emergency Medicine Applications?. Hitit Medical Journal, Cilt: 7 Sayı: 1, 118–123. https://doi.org/10.52827/hititmedj.1467697

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