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.

The increasing prevalence of carbapenem-resistant Enterobacteriaceae (CRE) poses a significant threat to public health. CRE, particularly Klebsiella pneumoniae, is associated with high morbidity and mortality owing to limited treatment options. This study identifies current research trends, major topics, and future directions in the scientific field of CRE. We systematically retrieved publications from the Web of Science database starting from 2020 to January 07, 2024. Using bibliometric tools such as VOSviewer, CiteSpace, and InCites, we analyzed publication counts, citation analysis, co-authorship, keyword co-occurrence, and thematic development to map the landscape and collaborative networks in the field. The search included keywords such as “carbapenem-resistant Enterobacteriaceae,” “carbapenemase-producing Enterobacteriaceae,” “carbapenemase-producing Klebsiella pneumoniae,” and “Klebsiella pneumoniae carbapenemase.” We reviewed 1946 articles. The largest contributors to the field were China, the United States, and Italy. This study highlights the significant increase in CRE-related publications, particularly in the context of hospital-acquired infections, antimicrobial resistance mechanisms, and novel therapeutic approaches. Keyword trend analysis revealed epidemiology, molecular biology, infection control, and public health implications, signaling a shift towards the broader impact of CRE in healthcare settings. This bibliometric analysis maps the current research landscape and identifies key areas for further research. Continued progress is essential to develop strategies to combat antimicrobial resistance and to improve patient outcomes.

Background: Retinal problems are critical because they can cause severe vision loss if not treated. Traditional methods for diagnosing retinal disorders often rely heavily on manual interpretation of optical coherence tomography (OCT) images, which can be time-consuming and dependent on the expertise of ophthalmologists. This leads to challenges in early diagnosis, especially as retinal diseases like diabetic macular edema (DME), Drusen, and Choroidal neovascularization (CNV) become more prevalent. OCT helps ophthalmologists diagnose patients more accurately by allowing for early detection. This paper offers a hybrid SE (Squeeze-and-Excitation)-Enhanced Hybrid Model for detecting retinal disorders from OCT images, including DME, Drusen, and CNV, using artificial intelligence and deep learning. Methods: The model integrates SE blocks with EfficientNetB0 and Xception architectures, which provide high success in image classification tasks. EfficientNetB0 achieves high accuracy with fewer parameters through model scaling strategies, while Xception offers powerful feature extraction using deep separable convolutions. The combination of these architectures enhances both the efficiency and classification performance of the model, enabling more accurate detection of retinal disorders from OCT images. Additionally, SE blocks increase the representational ability of the network by adaptively recalibrating per-channel feature responses. Results: The combined features from EfficientNetB0 and Xception are processed via fully connected layers and categorized using the Softmax algorithm. The methodology was tested on UCSD and Duke’s OCT datasets and produced excellent results. The proposed SE-Improved Hybrid Model outperformed the current best-known approaches, with accuracy rates of 99.58% on the UCSD dataset and 99.18% on the Duke dataset. Conclusion: These findings emphasize the model’s ability to effectively diagnose retinal disorders using OCT images and indicate substantial promise for the development of computer-aided diagnostic tools in the field of ophthalmology.

Background This study aims to evaluate the performance of ChatGPT in the medical specialization exam (MSE) that medical graduates take when choosing their postgraduate specialization and to reveal how artificial intelligence-supported education can increase the quality and academic success of medical education. The research aims to explore the potential applications and advantages of artificial intelligence in medical education and examine ways in which this technology can contribute to student learning and exam preparation. Methodology A total of 240 MSE questions were posed to ChatGPT, 120 of which were basic medical sciences questions and 120 were clinical medical sciences questions. A total of 18,481 people participated in the exam. The performance of medical school graduates was compared with ChatGPT-3.5 in terms of answering these questions correctly. The average score for ChatGPT-3.5 was calculated by averaging the minimum and maximum scores. Calculations were done using the R.4.0.2 environment. Results The general average score of graduates was a minimum of 7.51 in basic sciences and a maximum of 81.46, while in clinical sciences, the average was a minimum of 12.51 and a maximum of 80.78. ChatGPT, on the other hand, had an average of at least 60.00 in basic sciences and a maximum of 72.00, with an average of at least 66.25 and a maximum of 77.00 in clinical sciences. The rate of correct answers in basic medical sciences for graduates was 43.03%, while for ChatGPT was 60.00%. In clinical medical sciences, the rate of correct answers for graduates was 53.29%, while for ChatGPT was 64.16%. ChatGPT performed best with a 91.66% correct answer rate in Obstetrics and Gynecology and an 86.36% correct answer rate in Medical Microbiology. The least successful area for ChatGPT was Anatomy, with a 28.00% correct answer rate, a subfield of basic medical sciences. Graduates outperformed ChatGPT in the Anatomy and Physiology subfields. Significant differences were found in all comparisons between ChatGPT and graduates. Conclusions This study shows that artificial intelligence models such as ChatGPT can provide significant advantages to graduates, as they score higher than medical school graduates. In terms of these benefits, recommended applications include interactive support, private lessons, learning material production, personalized learning plans, self-assessment, motivation boosting, and 24/7 access, among a variety of benefits. As a result, artificial intelligence-supported education can play an important role in improving the quality of medical education and increasing student success.

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.

Objective: In this study, patients were divided into two groups. Patients with polycystic ovary syndrome (PCOS) and patients with polycystic ovary syndrome + Hashimoto's Thyroid (PCOS + HT). The effect of insulin resistance on ovarian volume in patients divided into two groups and the change in ovarian volume with the addition of HT to PCOS will be investigated. Material and methods: 46 PCOS patients and 46 PCOS patients diagnosed with HT were included in this study. A detailed medical history was taken from all participants. Polycystic ovary image was evaluated as below or above 10 ml and antral follicles were counted by transvaginal ultrasound. Insulin resistance of the patients was evaluated according to the fasting insulin (HOMA) index. Results: Insulin resistance was found to be associated with fasting insulin, HOMA index, body mass index and right ovarian volume in patients diagnosed with PCOS. Among the patients diagnosed with PCOS + HT, insulin resistance was found to be significantly correlated with fasting insulin, HOMA index, (BMI), (SHBG) and left ovarian volume. An increase in right ovarian volume was found in 37.5% of patients with PCOS without insulin resistance and in 76.3% of patients with insulin resistance. An increase in left ovarian volume was found in 35.7% of patients without insulin resistance diagnosed with PCOS + HT and in 68.8% of patients with insulin resistance. Conclusions: This study shows that ovarian volume should be evaluated in every PCOS patient in order to predict insulin resistance, which causes long-term metabolic diseases, and that all PCOS patients with increased ovarian volume should be investigated for insulin resistance. In addition, it has been observed that insulin resistance affects left ovarian volume in patients with PCOS + HT, whereas insulin resistance affects the volume of the right ovary more in patients with PCOS. At least one ovary has been found to be affected by long-term metabolic diseases. While there was a greater increase in ovarian volume with the addition of insulin resistance, no significant change was observed in the number of patients with increased ovarian volume (PCOS-58, PCOS + HT-57) with the addition of HT finding.