Radyoloji Yapay Zeka Çalışmalarında Veri Hazırlama Aşamaları
Synopsis
Tıbbi görüntüleme alanında yapay zeka (YZ) çalışmaları eksponansiyel bir hızla artmakta olup, bu teknolojilerin rutin klinik pratiğimize entegrasyonu artık kaçınılmaz bir gerçektir. Literatürdeki teorik performans başarılarının klinik sahaya güvenle aktarılabilmesi ve başarılı yapay zeka modellerinin geliştirilebilmesi için son derece ciddi, katı ve titiz bir metodolojik veri hazırlığı aşaması gerekmektedir. Çünkü modelin mimari karmaşıklığından ziyade, verinin kalitesi başarının temel belirleyicisidir ("Çöp girerse, çöp çıkar - GIGO" prensibi). Veri toplanması, anonimleştirme, matematiksel ön işleme, uyumlandırma ve etiketleme gibi aşamalar dışarıdan bakıldığında salt teknik ve bilgisayar mühendisliğine ait süreçler gibi görünse de; algoritmaya sunulan klinik "Referans Standart"ın oluşturulması ve verinin tıbbi doğruluğunun sağlanması doğrudan radyoloji uzmanının sorumluluğundadır. Bu nedenle, radyologların veri hazırlık sürecinin bizzat merkezinde bulunması klinik ve etik bir zorunluluktur. İlerleyen süreçte hastanelerde rutin kullanıma geçecek olan YZ modellerinin tanısal güvenilirliğini, dış dünya genellenebilirliğini ve olası sistematik yanlılıklarını denetleyebilmek; ancak hekimlerin bu veri hazırlama metodolojilerine, teknik parametrelere ve veri sızıntısı gibi metodolojik hatalara hakim olmasıyla mümkündür.
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