Tıpta Birleşik Öğrenme Yaklaşımı: Gizliliğin ve Model Kalitesinin Artırılması: Anlatısal Bir Derleme

Year 2025, Volume: 17 Issue: 3, 331 - 335, 29.10.2025

Cemil Çolak , Burcu Kayhan Tetik

Abstract

Birleşik Öğrenme (BÖ), ham verileri paylaşmadan birden fazla cihaz veya kurum arasında işbirlikçi model eğitimine olanak tanıyan ve böylece sağlık hizmetlerinde kritik gizlilik endişelerini gideren, merkezi olmayan bir makine öğrenimi paradigmasıdır. Bu derleme, BÖ'nün teknik ve etik zorlukları ele alırken hastalık tahminini, kişiselleştirilmiş bakımı ve sağlıkta eşitliği geliştirerek aile hekimliğini dönüştürme potansiyelini araştırıyor. BÖ'nün tıp alanındaki uygulamaları arasında hastalık tahmini, kişiselleştirilmiş tedavi, uzaktan hasta takibi ve kaynakların sınırlı olduğu ortamlarda sağlık eşitliğinin iyileştirilmesi yer almaktadır. BÖ, verdiği söze rağmen veri heterojenliği, hesaplama maliyetleri, etik kaygılar ve düzenleyici belirsizlik gibi zorluklarla karşı karşıyadır. Gelecekteki yönelimler arasında hibrit BÖ mimarileri, blok zinciri entegrasyonu, uç bilişim ve küresel sağlık girişimleri yer alıyor. Bu derleme, BÖ'nün aile hekimliği için dönüştürücü bir potansiyele sahip olduğu, hasta sonuçlarını iyileştirmek ve sağlık hizmeti eşitsizliklerini gidermek için gizliliği koruyan, veri odaklı çözümler sunduğu sonucuna varmıştır. Ancak başarısı, multidisipliner işbirliği ve hasta merkezli yönetim çerçeveleri aracılığıyla teknik, etik ve düzenleyici engellerin ele alınmasına bağlıdır.

Keywords

Birleşik Öğrenme , Gizliliğin Korunması , Makine Öğrenimi , Sağlık Bilişimi , Kişiselleştirilmiş Tıp , Sağlık Eşitliği

Federated Learning Approach in Medicine: Enhancing Privacy and Model Quality: A Narrative Review

Abstract

Federated Learning (FL) is a decentralized machine learning paradigm that enables collaborative model training across multiple devices or institutions without sharing raw data, thereby addressing critical privacy concerns in healthcare. This narrative review explores the potential of FL to transform family medicine by enhancing disease prediction, personalized care, and health equity while addressing technical and ethical challenges. FL's applications in medicine include disease prediction, personalized treatment, remote patient monitoring, and improving health equity in resource-limited settings. Despite its promise, FL faces challenges such as data heterogeneity, computational costs, ethical concerns, and regulatory ambiguity. Future directions include hybrid FL architectures, blockchain integration, edge computing, and global health initiatives. This review concludes that FL holds transformative potential for family medicine, offering privacy-preserving, data-driven solutions to improve patient outcomes and bridge healthcare disparities. However, its success depends on addressing technical, ethical, and regulatory barriers through multidisciplinary collaboration and patient-centric governance frameworks.

Keywords

Federated Learning , Privacy-Preserving , Machine Learning , Healthcare Informatics , Personalized Medicine , Health Equity

Ethical Statement

it didnt need ethical procedure

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