Üretken Yapay Zeka: Sağlıkta Hizmetlerinde Kullanımı, Üstün ve Zayıf Yanları

Abstract

Büyük dil modelleri (LLM) ve üretken yapay zeka (GenAI) sistemleri, sağlık hizmetlerinin sunum şeklini, tıbbi araştırmaların yürütülmesini ve hastaların bilgiyle etkileşim kurma biçimlerini dönüştürmeye başlamıştır. Bu modeller, genellikle erken tanı, yeni ilaçların geliştirilmesi, hasta eğitimi ve katılımı, hastalık salgını erken uyarı sistemleri, bireyselleştirilmiş tıp, genom madenciliği gibi alanlarda kullanılmakta ve pek çok alan için gelecek vaat etmektedir. Yapay zeka sistemlerinin sağlık hizmetlerini dönüştürücü potansiyelinin yanı sıra bir dizi etik sorunları, yönetsel ve operasyonel zorlukları da bulunmaktadır. Üretken AI'nın sağlık hizmetlerinde kullanımı sırasında mahremiyet tartışmaları, algoritmik yanlılıklar ve hasta güvenliği gibi sorunlar söz konusu olabildiğinden geliştiriciler, klinisyenler, politika yapıcılar ve kurumlara önemli sorumluluklar düşmektedir. Bu yazıda önce konuya ilişkin temel kavramlar açıklanmış, sonra tıpta ve bilimsel çalışmalarda yapay zeka kullanımına ilişkin mevcut durum değerlendirmesi yapılarak fırsatlar, olası riskler, geleceğe yönelik öngörüler ve öneriler özetlenmiştir.

Keywords

• Büyük Dil Modelleri (LLM)
• Üretken Yapay Zeka (GenAI)
• Makine Öğrenmesi (ML)
• Sağlık hizmetleri

Generative Artificial Intelligence: Its Use in Healthcare Services, Strengths and Weaknesses

Abstract

Large language models (LLMs) and generative artificial intelligence (GenAI) systems are beginning to transform the way healthcare is delivered, medical research is conducted, and patients’ interaction with medical information. These models are currently being used in areas such as early diagnosis, the development of new drugs, patient education and engagement, disease outbreak early warning systems, personalized medicine, and genomic mining, and hold great promise for many other fields. In addition to their transformative potential, AI systems in healthcare also present a range of ethical issues, managerial, and operational challenges. The use of generative AI in healthcare raises concerns such as privacy debates, algorithmic biases, and patient safety, thereby imposing significant responsibilities on developers, clinicians, policymakers, and institutions. This article first explains the basic concepts related to the topic, then provides an assessment of the current state of AI use in medicine and scientific research, and finally summarizes the opportunities, potential risks, future projections, and recommendations.

Keywords

• Large Language Models (LLMs)
• Generative Artificial Intelligence (GenAI)
• Machine Learning (ML)
• ); Healthcare services

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