Sağlık Sektöründe Karar Destek Araçları: İş Zekâsı, Makine Öğrenmesi, Derin Öğrenme ve Yapay Zeka Uygulamaları

Year 2024, Volume: 6 Issue: 2, 90 - 115

Muhammet Damar

https://doi.org/10.47899/ijss.1591168

Abstract

Bilgi ve iletişim teknolojileri tüm sektörleri olduğu gibi sağlık sektörünü de dönüştürmekte ve şekillendirmektedir. Bu muazzam dönüşüm içinde her geçen gün sağlık sektörü yönetim süreçlerinden günlük operasyonel süreçlerine kadar bilgi ve iletişim teknolojilerinden faydalanmakta ve karar süreçlerinde teknolojinin imkanlarından faydalanmaktadır. Çalışmamız kapsamında son yıllarda sağlık sektöründe önemi gittikçe artan iki farklı teknolojik gelişmeyi karar destek aracı olarak kapsamlı bir şekilde değerlendirmekteyiz. Yapay zeka ve iş zekası teknolojileri merkeze alınarak bu iki önemli kavramın kavramsal boyutları, sağlık sektörü için oluşturduğu değer kapsamlı bir şekilde değerlendirilmektedir. Yapay zeka içerisinde, makine öğrenmesi ve derin öğrenme gibi iki kritik kavram da değerlendirilmektedir. Makine öğrenmesi, yapay zeka, derin öğrenme ve iş zekası konuları pek çok farklı çalışmada farklı başlıklarda değerlendirmiştir. Fakat literatürde ilgili teknolojileri toplu olarak kapsamlı bir şekilde değerlendiren bir çalışmaya rastlanmamıştır. Aynı zamanda ilgili konu başlıklarının sağlık bilimleri alanında tartışıldığı bir çalışmaya da rastlanmamıştır. Çalışmamız bu boşluğu gidermeyi hedeflemektedir. Özellikle son yıllarda pek çok ülkenin yapay zeka konusunda önemli yatırımlar yaptığı günümüz koşullarında Türkiye’de bir ekonomik çıktı olarak yapay zeka uygulamaları konusunda ne tür kazanımlar elde edebileceğimizi konu kapsamında değerlendirilmektedir. Geleceğe dönük sağlık politikaları için kural koyucular ve politika yürütücüleri için çözüm önerileri ve örnek uygulama önerileri ortaya konmaktadır.

Keywords

Yapay zeka, iş zekası, karar destek aracı, sağlık enformatiği, sağlık sektörü

Thanks

Muhammet Damar, TÜBİTAK 2219 Uluslararası Doktora Sonrası Araştırma Burs Programı kapsamında Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) tarafından desteklenmiştir. Toronto Üniversitesi'ndeki Upstream Lab, MAP, Li Ka Shing Knowledge Institute'a mükemmel misafirperverliği için teşekkür ederiz.

Decision Support Tools in the Health Sector: Business Intelligence, Machine Learning, Deep Learning, and Artificial Intelligence Applications

Abstract

Information and communication technologies (ICT) are transforming and shaping the healthcare sector, as they are in all industries. In this immense transformation, the healthcare sector is increasingly utilizing ICT in management processes, daily operational procedures, and decision-making processes. This study comprehensively evaluates two significant technological advancements, which have gained increasing importance in recent years within the healthcare sector, as decision support tools. Artificial intelligence (AI) and business intelligence (BI) technologies are at the center of this evaluation, focusing on their conceptual dimensions and the value they create for the healthcare sector. Within AI, two critical concepts, machine learning and deep learning, are also discussed. Machine learning, AI, deep learning, and business intelligence have been addressed in numerous studies under various topics. However, there has been no study in the literature that comprehensively evaluates these technologies collectively. Additionally, no research has been found that discusses these topics specifically within the field of health sciences. This study aims to fill this gap. In light of the significant investments many countries have made in AI in recent years, this study also explores the potential economic benefits Turkey could achieve through AI applications. It presents solutions and example applications for policymakers and policy implementers regarding future healthcare policies.

Keywords

Artificial intelligence, business intelligence, decision support tool, health informatics, health sector

Thanks

Muhammet Damar, TÜBİTAK 2219 Uluslararası Doktora Sonrası Araştırma Burs Programı kapsamında Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) tarafından desteklenmiştir. Toronto Üniversitesi'ndeki Upstream Lab, MAP, Li Ka Shing Knowledge Institute'a mükemmel misafirperverliği için teşekkür ederiz.

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