Background: The integration of large language models (LLMs) in healthcare has generated significant interest due to their potential to improve diagnostic accuracy, personalization of treatment, and patient care efficiency. Objective: This study aims to conduct a comprehensive bibliometric analysis to identify current research trends, main themes and future directions regarding applications in the healthcare sector. Methods: A systematic scan of publications until 08.05.2024 was carried out from an important database such as Web of Science.Using bibliometric tools such as VOSviewer and CiteSpace, we analyzed data covering publication counts, citation analysis, co-authorship, co- occurrence of keywords and thematic development to map the intellectual landscape and collaborative networks in this field. Results: The analysis included more than 500 articles published between 2021 and 2024. The United States, Germany and the United Kingdom were the top contributors to this field. The study highlights that neural network applications in diagnostic imaging, natural language processing for clinical documentation, and patient data in the field of general internal medicine, radiology, medical informatics, health care services, surgery, oncology, ophthalmology, neurology, orthopedics and psychiatry have seen significant growth in publications over the past two years. Keyword trend analysis revealed emerging sub-themes such as clinical research, artificial intelligence, ChatGPT, education, natural language processing, clinical management, virtual reality, chatbot, indicating a shift towards addressing the broader implications of LLM application in healthcare. Conclusion: The use of LLM in healthcare is an expanding field with significant academic and clinical interest. This bibliometric analysis not only maps the current state of the research, but also identifies important areas that require further research and development. Continued advances in this field are expected to significantly impact future healthcare applications, with a focus on increasing the accuracy and personalization of patient care through advanced data analytics.

One of the most complex and life-threatening pathologies of the central nervous system is brain tumors. Correct diagnosis of these tumors plays an important role in determining the treatment plans of patients. Traditional classification methods often rely on manual assessments, which can be prone to error. Therefore, multiple classification of brain tumors has gained significant interest in recent years in both the medical and computer science fields. The use of artificial intelligence and machine learning, especially in the automatic classification of brain tumors, is increasing significantly. Deep learning models can achieve high accuracy when trained on datasets in diagnosis and classification. This study examined deep learning-based approaches for automatic multi-class classification of brain tumors, and a new approach combining deep learning and quantum genetic algorithms (QGA) was proposed. The powerful feature extraction ability of the pre-trained EfficientNetB0 was utilized and combined with this quantum genetic algorithms, a new approach was proposed. It is aimed to develop the feature selection method. With this hybrid method, high reliability and accuracy in brain tumor classification was achieved. The proposed model achieved high accuracy of 98.36% and 98.25%, respectively, with different data sets and significantly outperformed traditional methods. As a result, the proposed method offers a robust and scalable solution that will help classify brain tumors in early and accurate diagnosis and contribute to the field of medical imaging with patient outcomes.

Diabetic retinopathy (DR) is a leading cause of visual impairment and blindness worldwide, necessitating early detection and accurate diagnosis. This study proposes a novel framework integrating Generative Adversarial Networks (GANs) for data augmentation, denoising autoencoders for noise reduction, and transfer learning with EfficientNetB0 to enhance the performance of DR classification models. GANs were employed to generate high-quality synthetic retinal images, effectively addressing class imbalance and enriching the training dataset. Denoising autoencoders further improved image quality by reducing noise and eliminating common artifacts such as speckle noise, motion blur, and illumination inconsistencies, providing clean and consistent inputs for the classification model. EfficientNetB0 was fine-tuned on the augmented and denoised dataset. The framework achieved exceptional classification metrics, including 99.00 % accuracy, recall, and specificity, surpassing state-of-the-art methods. The study employed a custom-curated OCT dataset featuring high-resolution and clinically relevant images, addressing challenges such as limited annotated data and noisy inputs. Unlike existing studies, our work uniquely integrates GANs, autoencoders, and EfficientNetB0, demonstrating the robustness, scalability, and clinical potential of the proposed framework. Future directions include integrating interpretability tools to enhance clinical adoption and exploring additional imaging modalities to further improve generalizability. This study highlights the transformative potential of deep learning in addressing critical challenges in diabetic retinopathy diagnosis.

AMAÇ: Bu çalışmada, yapay zekanın (YZ) sağlık alanında hayatımızın ayrılmaz bir parçası haline gelmesi ile birlikte tıp öğrencilerinin yapay zeka teknolojileri ve yapay zekanın tıptaki uygulamaları konusunda algıladıkları hazır bulunuşlukları araştırılmıştır. GEREÇ VE YÖNTEM: Bu araştırma Afyonkarahisar Sağlık Bilimleri Üniversitesi’nde (AFSÜ) öğrenim gören 1-3. sınıf öğrencilerine uygulanmıştır. Çalışmaya katılmayı kabul eden 203 öğrenci örnekleme alınmıştır. Veriler araştırmacılar tarafından hazırlanan sosyodemografik form ve tıbbi yapay zeka hazır bulunuşluluk ölçeği ile toplanmıştır. Verilerin analizi R.4.3.2 ortamı kullanılarak yapılmıştır. BULGULAR: Çalışmada yer alan 203 öğrencinin 121’i (% 59,6) kız öğrenci, 82’si (% 40,4) erkek öğrencidir. Tıbbi yapay zeka bilişsel hazır bulunuşluluğunun erkek öğrencilerde, kız öğrencilere göre daha fazla olduğu ve bunun istatistiksel olarak anlamlı olduğu görülürken, bilişsel, öngörü ve etik hazır bulunuşluluklarında kız ve erkek öğrenciler arasında anlamlı farklılık bulunmamıştır. Ayrıca, öğrencilerin tıbbi yapay zeka bulunuşlulukları öğrencilerin sınıflarına göre önemli bir farklılık göstermemektedir. SONUÇ: Öğrenciler için yapay zeka teknolojileri ve uygulamaları konusunda algılanan hazır bulunuşluk düzeyleri değerlendirildiğinde, tıbbi yapay zeka hazır bulunuşluluk ölçeği'nin alt boyutlarında genel olarak puanların düşük olduğu görülmüştür. En düşük puana bilişsel alt boyut sahiptir. En yüksek puan ise etik hazır bulunuşlulukta görülmüştür. Sonuç olarak elde edilen bu puanlar, öğrenci ihtiyaçlarının değerlendirilmesinde ve tıp eğitiminde değerli bir müfredatın geliştirilmesi için bir araç olarak kullanılabilir.