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Journals / Adıyaman Üniversitesi Sağlık Bilimleri Dergisi / 2026 / Cilt: 11 Sayı: 2

Transforming healthcare and education: The impact of deep learning in medical informatics and training

Sağlık hizmetleri ve eğitimin dönüşümü: Derin öğrenmenin tıp bilişimi ve eğitimdeki etkisi

Emek Güldoğan(INONU UNIVERSITY)0000-0002-5436-8164
Cemil Çolak(INONU UNIVERSITY)0000-0001-5406-098X
Pages
191–204
Publication Date
2025-08-30
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AI Summary & Key Takeaways

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

Such changes are leading us into a revolution in healthcare and medical education with deep learning, where the efficiency, personalization and evidence-based are improved.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Artificial intelligence (AI) and deep learning have quickly developed for image-based diagnostics, disease prediction and clinical decision support.
📊
Key Findings & Data:The future directions show that explainable AI (XAI), federated learning and ethical frameworks are required to be integrated for enhancing transparency and reliability.
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Conclusion & Impact:Such changes are leading us into a revolution in healthcare and medical education with deep learning, where the efficiency, personalization and evidence-based are improved.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Derin öğrenme · Tıp bilişimi · Yapay zeka

Abstract

Artificial intelligence (AI) and deep learning have quickly developed for image-based diagnostics, disease prediction and clinical decision support. Medical informatics/education in medicine have been huge changes caused by these technologies. To analyze such a complex biomedical data, deep learning architectures are employed. This narrative review discusses how deep learning has transformed medical education and informatics, as it is used in medical imaging, genomic analysis, predictive analytics, and smart monitoring systems. In medical education, deep learning is employed for interactive simulations, automated grading, and individual learning; in informatics, for diagnosis accuracy, decision-making, as well as patient care. The future directions show that explainable AI (XAI), federated learning and ethical frameworks are required to be integrated for enhancing transparency and reliability. Such changes are leading us into a revolution in healthcare and medical education with deep learning, where the efficiency, personalization and evidence-based are improved.

Özet

Yapay zeka (AI) ve derin öğrenme, görüntü tabanlı teşhis, hastalık tahmini ve klinik karar desteği için hızla gelişmiştir. Tıp bilişimi/eğitiminde bu teknolojilerin neden olduğu büyük değişiklikler olmuştur. Böylesine karmaşık biyomedikal verileri analiz etmek için derin öğrenme mimarileri kullanılmaktadır. Bu anlatı incelemesi, derin öğrenmenin tıbbi görüntüleme, genomik analiz, tahmine dayalı analitik ve akıllı izleme sistemlerinde kullanıldığı şekliyle tıp eğitimi ve bilişimini nasıl dönüştürdüğünü tartışmaktadır. Tıp eğitiminde derin öğrenme, etkileşimli simülasyonlar, otomatik not verme ve bireysel öğrenme için; bilişimde ise teşhis doğruluğu, karar verme ve hasta bakımı için kullanılmaktadır. Geleceğin yönelimleri, şeffaflığı ve güvenilirliği arttırmak amacıyla açıklanabilir yapay zeka (XAI), federatif öğrenme ve etik çerçevelerin entegre edilmesi gerektiğini göstermektedir. Bu tür değişiklikler sağlık hizmetlerinde ve tıp eğitiminde derin öğrenme ile verimliliğin, kişiselleştirmenin ve kanıta dayalı olmanın geliştirildiği bir devrime doğru yönlendirmektedir.

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Cite This Article

Güldoğan, E., Çolak, C. (2025). Transforming healthcare and education: The impact of deep learning in medical informatics and training. Adıyaman Üniversitesi Sağlık Bilimleri Dergisi, Cilt: 11 Sayı: 2, 191–204. https://doi.org/10.30569/adiyamansaglik.1643217

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