Tıp Eğitiminde Yapay Zeka: Asistan Hekimlerin Kullanım Alanları ve Algıları

Year 2025, Volume: 24 Issue: 74, 46 - 57, 22.12.2025

Hilal Hatice Ülkü , Selcen Öncü , Fulya Torun

https://doi.org/10.25282/ted.1659294

https://izlik.org/JA36FJ84MJ

Abstract

Amaç: Yapay zeka (YZ) tıp eğitiminde kişiselleştirilmiş öğrenme deneyimleri sunarak asistan hekimlerin bilgiye erişimini kolaylaştırmakta ve karar alma süreçlerini desteklemektedir. Buna rağmen, asistan hekimlerin YZ kullanımına yönelik görüşleri ve hangi YZ aracını ne amaçla kullandıklarına yeterince bilinmemektedir. Bu çalışma ile asistan hekimlerin YZ kullanımına ilişkin görüşleri ve deneyimlerinin belirlenmesi amaçlanmıştır.
Gereç ve Yöntem: Bu çalışma nitel araştırma desenlerinden durum çalışması kullanılarak tasarlanmıştır. Çalışma Temmuz- Eylül 2024 tarihleri arasında yürütülmüş ve maksimum çeşitlilik örneklemesi ile farklı branşlardan 18 asistan hekim çalışmaya dahil edilmiştir. Yarı yapılandırılmış görüşme formu aracılığıyla toplanan veriler, MAXQDA 2022 yazılımı kullanılarak içerik analizi ile değerlendirilmiştir. Kodları açıklayıcı nitelikte alıntılar kullanılmıştır.
Bulgular: Asistan hekimlerin en yaygın YZ kullanım amacı literatür tarama (n=11) olarak belirlenmiştir. En sık kullanılan YZ aracı ChatGPT (n=9) olmuştur. Asistanlar YZ’nın tıp eğitimine entegre edilmesini (n=2), simülasyon (n=1) ve teorik dersler (n=1) gibi alanlarda kullanılmasını ifade etmişlerdir. Bazı asistanlar YZ’nın eğitimi olumsuz etkileyeceğini (n=3) belirtmiştir. Gelecekte YZ’dan en fazla beklenti, radyolojik/laboratuvar sonuçlarını yorumlama (n=4) ve hasta değerlendirmesi yapma (n=3) yönünde olmuştur. Dahili bilim asistanları hasta değerlendirme (n=3) ve ön tanı listesi oluşturma (n=2) beklentilerini dile getirirken, cerrahi bilim asistanları robotik cerrahinin gelişmesi (n=2) ve hasta gözleminin kolaylaştırılması (n=1) yönünde görüş bildirmiştir. Temel bilim asistanları YZ’yı görsel materyal hazırlama (n=3), çeviri yapma (n=2) ve soru oluşturma (n=1) gibi eğitim amaçlı kullanırken, cerrahi bilim asistanları hatırlatma aracı olarak (n=1) ve hızlı olmasından dolayı (n=1) tercih etmiştir. Temel bilim asistanları YZ’nın tıpta hata oranını düşürebilecek uygulama sunduğu (n=1) ve eğitimi görselleştirdiği (n=1) ve kolaylaştığı (n=1) cevaplarını verirken, dahili bilim asistanları YZ’da hekimin duygusal boyutu olmadığı (n=1), teorik derslerin YZ ile işlenebileceğini belirtmiştir.
Sonuç: Asistan hekimler, YZ’yı çalışma hayatlarını kolaylaştırmasını, organizasyon ve planlama desteği sağlamasını, rehberlik sunmasını ve hızlı olmasını beklemektedir. Mevcut kullanım çoğunlukla bilgiye erişim ve literatür tarama ile sınırlı kalmaktadır. YZ'nın klinik süreçlerde kullanımı konusunda asistanların beklentileri hasta değerlendirme, tetkik yorumlama ve ön tanı oluşturma yönünde şekillenmiştir. Çalışmanın bulguları, YZ konusunda asistan hekimlerin farkındalığını artırmak için lisansüstü tıp eğitiminde bilgilendirici eğitimlerin düzenlenmesi ve YZ kullanımının desteklenmesi gerektiğini ortaya koymaktadır.

Keywords

Yapay Zeka , Tıp Eğitimi , Asistan Hekim , Nitel Çalışma

Ethical Statement

Aydın Adnan Menderes Üniversitesi Tıp Fakültesi Girişimsel Olmayan Klinik Araştırmalar Etik Kurulu’ndan Karar No: 3 2024/155 Sayılı izin alınmıştır.

Supporting Institution

Yok

Thanks

Çalışmaya gönüllü olarak katılan tüm asistan hekimlere teşekkür ederiz.

Artificial Intelligence in Medical Education: Uses and Perceptions of Residents

https://izlik.org/JA36FJ84MJ

Abstract

Objective: Artificial intelligence (AI) facilitates residents' access to information and supports their decision-making processes by providing personalized learning experiences in medical education. However, residents views on the use of AI and which AI tool they use and for what purpose are not sufficiently known. The aim of this study was to determine the opinions and experiences of residents regarding the use of AI.
Materials and Method: This study was designed using a case study, one of the qualitative research designs. The study was conducted between July and September 2024 and 18 residents from different branches were included in the study with maximum diversity sampling. The data collected through a semi-structured interview form were evaluated by content analysis using MAXQDA 2022 software. Quotations were used to explain the codes.
Results: The most common purpose of AI use by residents was determined as literature search (n=11). The most frequently used AI tool was ChatGPT (n=9). Residents expressed that AI should be integrated into medical education (n=2) and used in areas such as simulation (n=1) and theoretical courses (n=1). Some residents stated that AI would negatively affect education (n=3). The highest expectations from AI in the future were to interpret radiologic/laboratory results (n=4) and to perform patient assessment (n=3). Internal science residents expressed their expectations for patient assessment (n=3) and creating a preliminary diagnosis list (n=2), while surgical science residents expressed their opinions for the development of robotic surgery (n=2) and facilitating patient observation (n=1). Basic science residents used AI for educational purposes such as preparing visual materials (n=3), translating (n=2) and creating questions (n=1), while surgical science residents preferred it as a reminder tool (n=1) and because it was fast (n=1). Basic science residents answered that AI offers applications that can reduce the error rate in medicine (n=1) and visualizes education (n=1) and makes it easier (n=1), while internal science residents stated that AI does not have an emotional dimension of the physician (n=1) and that theoretical courses can be taught with AI.
Conclusion: Residents expect AI to make their working life easier, provide organizational and planning support, offer guidance and be fast. Current use is mostly limited to access to information and literature review. The expectations of residents regarding the use of AI in clinical processes are shaped in the direction of patient assessment, test interpretation and preliminary diagnosis. The findings of the study reveal that informative trainings should be organized in postgraduate medical education to increase the awareness of residents about AI and the use of AI should be supported.

Keywords

Artificial Intelligence , medical education , resident , qualitative research

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