Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Tuncer, Metin(5)
- Tuncer, Gülsüm Zekiye(3)
- Öztekin, Ertekin(3)
- Abdel-Fattah, Abdul-Rahman(1)
- Akdeniz, Canberk(1)
- Al-Taib, Asmaa Abduh(1)
- Altas, Ismail Hakki(1)
- Arslan, Adem(1)
- Aslan, Muhammed Emin(1)
- Avci, Ibrahim(1)
- Bayrakdaroglu, Serdar(1)
- Belhaouari, Samir Brahim(1)
- Ceylan, Halil Ibrahim(1)
- Cin, Aynur(1)
- Coşar, Dilan Aktepe(1)
- Demirağ, Hatice(1)
- Dergaa, Ismail(1)
- Dhahbi, Wissem(1)
- Duman, Zekiye Çetinkaya(1)
- Erdugan, Fuat(1)
- Erol, Ismail(1)
- Gezgin, Deniz Mertkan(1)
- Gül, Fatih(1)
- Gül, Uğur(1)
- Kadir Yalçın(1)
- Karakaya Suzan, Ozge(1)
- Kedersiz, Sena(1)
- Kirmizibiber, Ahmet(1)
- Kocan, Mahmut(1)
- Kordestani, Arash(1)
- Koyuncu, Oğuz(1)
- Kulakaç, Nurşen(1)
- Medeni, Ihsan Tolga(1)
- Muntean, Raul Ioan(1)
- Nurcan Şimşek(1)
- Nurlu, Onur(1)
- Oghazi, Pejvak(1)
- Omri, Abdelfatteh El(1)
- Onur, İzmir(1)
- Ozyildirim, BM(1)
Yayınlar
- Doktora TeziAçık Erişim2026Yapay zekâ tartışmalarına teolojik yaklaşım
zeynep Şeren
Hızla gelişen yapay zekâ teknolojisi insanın en yeni ve etkin başarısı olarak görülmektedir. Yapay zekâ hayatın her alanına nüfuz ederek birçok alanda ihtiyaca çözümler üretebilen yapısı yanında, insana ve onun inancına dair bir takım iddialara kaynaklık etmesi yönünden tartışılmaya muhtaç bir konu haline gelmiştir. Özellikle yapay zekâ teknolojilerini teolojik ve ontolojik boyutlarıyla Kelam ilmi perspektifinden inceleyen bu çalışma, yapay zekânın bilinç ve irade kazanımına dair iddialarını, insanın ontolojisi ve teklife muhatap olması çerçevesinde incelemektedir. Ayrıca epistemolojik yeterliliği, transhümanizm gibi din karşıtı ideolojilerce tevhide aykırı şekillerde kullanılmak üzere desteklenmesi onu araçtan öte bir varlık olarak gösteren tartışmalar içerisine yerleştirmektedir. Yapay zeka çalışmalarına artan destek ve önem onu gelecekte daha bilinçli durumlar gösteren genel yapay zekâya ulaştıracağına dair kanaat oluşturmaktadır. Bu durum, Kelam ilminin konuya dair güncel ve dinamik argümanlar üretmesini kaçınılmaz kılmaktadır. Kelam ilmi İslâm akaidi çerçevesinde dinin özüne sadık açıklamaları ve savunmaları ile insanı kulluk dairesinden çıkaracak itikadi iddiaların oluşturduğu sapmaları önlemektedir. Çalışma yapay zekânın itikadın temelini oluşturan tevhit ve ahirete dair sapmalara sebebiyet verebilecek iddiaları konu edinmektedir. Ölümsüzlük, dinin yerini teknolojinin alması, insanın yaratıcı olarak konumlanması bunlardan bir kaçıdır. İslâm kelamının ve yapay zekânın iddialarının karşılaştırılması için kavram açıklamalarına ve insan mahiyetine dair kaynak taraması yapılmıştır. Henüz kesin, açık bir açıklaması olmayan bilinç ve ruh gibi kavramların çalışılması yanında yapay zeka sistemlerindeki hızlı gelişmeler konu hakkındaki kanaati belirlemeyi zorlaştıran etmenler olmuştur. Buna rağmen yapay zekâyı tanımak, kelami argümanlarını tartışmak ve doğruluk değerlerini belirlemek açısından çalışma değerlidir.
- Yüksek Lisans TeziAçık Erişim2026Zihin felsefesinde düşünce deneylerinin epistemik statüsü
Yasemin Revi
Bu tez, düşünce deneylerinin zihin felsefesindeki yerini ve felsefi bilginin oluşumuna sağladıkları katkıyı bütüncül bir bakış açısıyla değerlendirmeyi amaçlamaktadır. Özellikle, bilincin doğasının, kişisel kimliğin, zihin ile dış dünya arasındaki ilişkinin ve yapay zekânın imkânının tartışıldığı zihin felsefesinde temel bir yöntem olarak kullanılan düşünce deneylerinin, varsayımsal senaryolara dayanması nedeniyle bilgi üretme kapasiteleri, gerçekliği temsil etme biçimleri ve felsefi argümanlardaki epistemik değerleri tartışma konusudur. Bu kapsamda çalışmada öncelikle düşünce deneylerinin tarihsel gelişimi ele alınmış, Antik Çağ’dan modern döneme uzanan süreçte Platon’un Mağara Alegorisi, Theseus’un Gemisi, İbn Sînâ’nın Uçan Adamı ve Descartes’ın Kötü Cin Hipotezi gibi öne çıkan örnekler incelenmiştir. Ardından zihin felsefesinin temel problemleri değerlendirilmiş ve düşünce deneylerinin bu problemlerin açıklanması, kavramsallaştırılması ve tartışılmasındaki işlevi ortaya konmuştur. Bu doğrultuda Frank Jackson’ın Mary’nin Odası, David Chalmers’ın Zombi Argümanı, Hilary Putnam’ın Kavanozdaki Beyni, Alan Turing’in Turing Testi ve John Searle’ün Çince Odası düşünce deneyleri ayrıntılı biçimde analiz edilmiştir. Son olarak bu düşünce deneyleri kavramsal tutarlılık, tasavvur edilebilirlik, metafiziksel olanaklılık, sezgisel güvenilirlik, açıklayıcı güç, karşı senaryolara dayanıklılık ve teori seçimindeki rolleri gibi epistemik ölçütler çerçevesinde değerlendirilmiştir. Böylece, düşünce deneylerinin kavramsal analizin ötesinde felsefi bilgi üretimine özgün bir katkı sağlayıp sağlamadığı sorgulanmıştır.
- MakaleAçık Erişim2026AIlessphobia in Education and Academic Procrastination among Undergraduate Students in Sports Sciences: A Cross-Sectional Study
Gezgin, Deniz Mertkan; Erdugan, Fuat; Nurlu, Onur; Kedersiz, Sena; Temel, Ahmet
AIlessphobia in education refers to the anxiety, fear, and feeling of inadequacy that students experience when they are unable to access or use artificial intelligence (AI) tools during academic tasks. AIlessphobia in education, which has been proposed as a relatively new psychological concept in educational settings, may be related to students' self-regulation difficulties and procrastination tendencies. In this context, this study examined the relationship between AIlessphobia in education and academic procrastination in students of the Faculty of Sport Sciences. The study was designed as a cross-sectional study and included 313 undergraduate students selected through convenience sampling. Data were collected using a demographic information form, the AIlessphobia in Education Scale and the Academic Procrastination Scale. Descriptive statistics, Pearson correlation analysis and simple linear regression were used to analyse the data. The findings indicated that AIlessphobia in education and its subdimensions were positively associated with academic procrastination. In the regression model, AIlessphobia in education was significantly associated with academic procrastination and explained 5.2% of the variance. Although the explained variance was small, the findings suggest that higher AIlessphobia in education levels are associated with higher academic procrastination. These findings contribute to the emerging literature on AI-related psychological constructs in education and indicate that academic procrastination may be a significant correlate of AIlessphobia in education among university students.
- MakaleAçık Erişim2026Scopus SJRQ1The Mediating and Moderating Roles of Innovation Climate in the Relationship Between Nurses’ Attitudes Toward Artificial Intelligence, Creative Self-Efficacy, and Clinical Competence
Karakaya Suzan, Ozge; Gül, Uğur; Koyuncu, Oğuz; Akdeniz, Canberk
Background: Artificial intelligence (AI) is increasingly integrated into health care; however, how nurses’ attitudes toward AI and creative self-efficacy are associated with perceived clinical competence within different organizational contexts remains unclear. Aim: To examine whether perceived climate for innovation statistically mediates the association between nurses’ attitudes toward AI and perceived clinical competence and moderates the association between creative self-efficacy and perceived clinical competence. Methods: This cross-sectional study included 233 nurses recruited online by convenience sampling from hospitals in Türkiye between January and March 2026. Self-report measures assessed attitudes toward AI, creative self-efficacy, perceived climate for innovation, and perceived clinical competence. Pearson correlations and PROCESS Models 4 and 1 with 5000 bootstrap samples tested statistical mediation and moderation. Adjusted analyses included age, gender, previous AI-related training and AI use, educational level, professional experience, and institution type. Results: Attitudes toward AI were indirectly associated with perceived clinical competence through perceived climate for innovation; the adjusted indirect association remained significant (B = 0.0700, SE = 0.0333, 95% CI [0.0173, 0.1453]), consistent with partial statistical mediation. Perceived climate for innovation also moderated the association between creative self-efficacy and perceived clinical competence after adjustment (interaction B = −0.1672, SE = 0.0447, 95% CI [−0.2552, −0.0792]). This positive association became weaker as perceived climate for innovation increased. Conclusion: Attitudes toward AI, creative self-efficacy, perceived climate for innovation, and perceived clinical competence were significantly associated, and the statistical mediation and moderation patterns remained after adjustment. The cross-sectional, self-report data do not establish temporal or causal mechanisms or effects on objectively measured clinical performance or patient outcomes. Implications for Nursing Management: Nurse managers may consider supporting AI literacy, safe AI use, team-based learning, and innovation-supportive work environments. These strategies should be evaluated prospectively using objective clinical and patient outcomes before their effectiveness is inferred.
- MakaleYalnız künye2026Scopus SJRQ2Integrating Artificial Intelligence Into Nursing Care Planning: A Comparative Evaluation of Psychosocial Care for Patients With a Stoma
Kulakaç, Nurşen; Tuncer, Metin
This methodological study evaluated and compared the content quality and appropriateness of nursing care plans developed by nurses and artificial intelligence (AI) for addressing the psychosocial problems of patients with stomas. Two nursing care plans addressing the psychosocial needs of a patient with a stoma were developed: 1 generated by nurses and 1 generated by an AI system. The content quality and appropriateness of the care plans were independently evaluated by 12 experts, including nursing academics and clinical nurses, using the DISCERN measurement tool. Inter-rater reliability was assessed using the intraclass correlation coefficient (ICC). The mean DISCERN score of the nurse-generated care plan was significantly higher than that of the AI-generated care plan (P=.008). Inter-rater agreement demonstrated good agreement. The findings indicate that nurse-generated care plans demonstrate superior content quality and overall integrity compared to AI-generated plans. AI-generated care plans exhibit potential as supportive tools in nursing care planning.
- MakaleAçık Erişim2026Scopus SJRQ2The Effect of a Psychiatric Nurse–Led AI-Based Online Group Guided Self-Help Program on Eating Behaviours and Body Image among Overweight and Obese Women Nurses: A Randomized Controlled Trial
Tuncer, Gülsüm Zekiye; Duman, Zekiye Çetinkaya; Tuncer, Metin
Aim: This study aimed to evaluate the effectiveness of a psychiatric nurse–led, AI-supported online group guided self-help programme on eating behaviours, body image, and body mass index (BMI) among overweight and obese women nurses. Method: A randomised controlled experimental design with pre-test, post-test, and three-month follow-up assessments was employed. Forty-four overweight and obese women nurses were randomly assigned to intervention (n = 22) and control (n = 22) groups. The intervention consisted of an 8-week online group guided self-help programme supported by a large language model (LLM), under the leadership of a psychiatric nurse. Participants did not interact directly with the LLM; all AI-generated feedback was clinically reviewed and delivered by the researcher. Results: The intervention group demonstrated significant reductions in emotional eating and external eating, along with significant improvements in body image scores and adaptive coping strategies. A significant decrease in BMI was also observed from baseline to three-month follow-up. Between-group comparisons at post-test and follow-up indicated statistically significant differences favouring the intervention for eating behaviours and selected body image–related outcomes, whereas between-group differences in BMI were not statistically significant. Conclusion: A multi-component online group guided self-help intervention incorporating structured AI-assisted feedback under psychiatric nurse leadership may represent a promising approach to improving eating behaviours, body image, and weight-related outcomes among overweight and obese women nurses.
- MakaleAçık Erişim2026Scopus SJRQ1Health sciences students’ attitudes toward artificial intelligence: predictors of ethical awareness, clinical decision-making, and public health perceptions-a cross-sectional study
Unal, Cihan; Şahin, Selim
This study investigates health sciences students’ attitudes toward artificial intelligence (AI) and the implications for ethical awareness, clinical decision-making, and public health. A cross-sectional survey was conducted between April 27 and May 15 2025, with 668 students from five departments at Gümüşhane University, employing the validated Artificial Intelligence Attitude Scale, which measures benefits, risks, and use, alongside 12 binary-response items assessing ethical, clinical, and public health judgments. Descriptive statistics, t-tests, ANOVA, and logistic regression analyses were applied. Findings indicate that students perceive AI as highly beneficial (M = 4.05) but also associate it with notable risks (M = 2.52; where lower scores indicate a higher level of perceived risk due to reverse coding). Logistic regression analyses revealed that risk perception (reverse-coded; higher scores indicating lower perceived risk) was the most consistent predictor across all dimensions. Specifically, students with lower perceived risk were significantly more likely to reject concerns regarding patient privacy (OR = 2.55, 95% CI [2.03–3.21], p < 0.001), dismiss the idea that relying on AI instead of human expertise is problematic (OR = 1.57, 95% CI [1.25–1.96], p < 0.001), and reject the notion that AI systems may harm public health (OR = 2.52, 95% CI [1.98–3.20], p < 0.001). While participants endorsed AI’s potential in enhancing patient safety, chronic disease management, and preventive care, they expressed significant concerns about privacy, legal responsibility, and a potential weakening of patient–clinician communication. Gender, academic discipline, and prior AI use further differentiated attitudes. The results highlight a dual perception of AI as both an opportunity and a threat, emphasizing that successful integration in healthcare requires not only technical competence but also ethical, legal, and communicative safeguards.
AtıfScopus1WoS1OpenAlex2
İncele - MakaleYalnız künye2026Scopus SJRQ1Nurses' artificial intelligence anxiety levels, their attitudes towards artificial intelligence, and their skills in acting humanistically in nursing practices
Cin, Aynur; Coşar, Dilan Aktepe; Demirağ, Hatice
Aim This study aims to investigate the levels of artificial intelligence-related anxiety among nurses, their attitudes towards the use of AI in clinical settings, their ability to maintain humanistic approaches in nursing care, and the interrelationships among these variables. Design This study was designed as a descriptive and cross-sectional pilot study. Method The sample of the descriptive study consisted of 267 nurses working in X and Y hospitals in Gümüşhane province. The data of the research was collected between December 2023 and May 2024 using the “Nurse Introduction Form”, “Artificial Intelligence Anxiety Scale”, “General Attitude Scale towards Artificial Intelligence” and “Humanistic Practice Ability of Nursing”. Results Total “Artificial Intelligence Anxiety Scale” mean scores were found to be higher in nurses who were women, and had more than five years of working experience ( p < 0.05). It was determined that the total “General Attitude Scale Towards Artificial Intelligence” score averages of nurses working in surgical clinics were higher than those working in pediatric clinics ( p < 0.05). Positive “General Attitude Scale Towards Artificial Intelligence” mean scores were higher in nurses who had a postgraduate degree, worked in a surgical clinic, and had five years or less experience ( p < 0.05). Again, the total mean of the “ Humanistic Practice Ability of Nursing “ was higher in nurses who had a bachelor's degree or less, and worked in management and surgery clinics ( p < 0.05). Conclusion It was found that nurses' artificial intelligence anxiety, general attitudes towards artificial intelligence, and ability to act humanistally varied according to some sociodemographic characteristics.
AtıfScopus1WoS1OpenAlex1İncele - Derleme MakaleAçık Erişim2026Scopus SJRQ1Artificial-intelligence–based developmental screening for pre-school children (ages 3–6): a scoping review of deployed tools, clinical validity, equity, and implementation in guidance-center settings
Arslan, Adem; Gül, Fatih
Background – Early identification of neurodevelopmental conditions during the pre-school years (ages 3–6) is critical for timely intervention, yet traditional questionnaire-based screeners suffer from low positive predictive value, late detection, and inequitable access. Artificial intelligence (AI) and machine-learning (ML) methods promise more objective, scalable identification, and two devices (Cognoa Canvas Dx, 2021; EarliPoint Evaluation, 2023) are already US Food and Drug Administration (FDA)-authorized. Recent reviews summarize the technical literature, but few address how deployed tools perform on clinical validity, equity, and real-world implementation, particularly outside high-income, English-speaking settings. Methods – Following Joanna Briggs Institute methodology and the PRISMA extension for scoping reviews (PRISMA-ScR), we searched seven databases (1 January 2020–15 June 2026) and charted primary validation studies of AI/ML developmental-screening tools for children aged 72 months or younger. We mapped tools by modality, diagnostic performance, regulatory status, geographic origin, and, as a structured analytic axis, demographic subgroup (equity) reporting and deployment readiness. Results – Sixteen primary AI-tool studies met inclusion, spanning eight modality classes (eye-tracking, multimodal questionnaire-plus-video, computer-vision home/clinical video, tablet/smartphone digital phenotyping, voice/acoustic ML, electronic-health-record risk prediction, questionnaire-based ML, and wearable sensors) and eight countries (USA, South Korea, Australia, Switzerland, France, Israel, China, Vietnam). Reported sensitivities ranged from approximately 75 to 99.2% and specificities from 78.9 to 95.5%. Only one tool demonstrated stable performance across sex, race, and ethnicity; subgroup-stratified validation was otherwise near-absent. No CE-marked autism-specific screening device was identified. Conclusions – The evidence base has broadened in modality and geography but remains autism-concentrated and equity-blind, with a wide gap between analytic accuracy and primary-care deployment. We propose equity validation, deployment-readiness reporting, and an implementation pathway for low-resource guidance-center settings as priorities.
- MakaleAçık Erişim2026Scopus SJRQ1YOLOv8 computer vision for automated offside detection in professional football validated through supervised learning
Abdel-Fattah, Abdul-Rahman; Belhaouari, Samir Brahim; Ceylan, Halil Ibrahim; Bayrakdaroglu, Serdar; Muntean, Raul Ioan; Dhahbi, Wissem; Dergaa, Ismail; Omri, Abdelfatteh El
Offside calls are integral to maintaining fairness and competitive integrity in football, yet current officiating processes are constrained by limitations in accuracy despite technological advancements. This investigation developed and validated an automated offside detection system using YOLOv8 object detection architecture to address documented limitations of Video Assistant Referee (VAR) systems in offside decision accuracy. Supervised learning validation employed 400 annotated instances from professional match footage. The YOLOv8 medium variant was configured for four-class detection, with a focus on extreme player positions, reducing computational complexity by approximately 70% relative to comprehensive player tracking while accepting accuracy trade-offs in marginal scenarios. The system was optimized for computational efficiency through highly accurate tracking of player positions. Data were partitioned into 40% (n = 160) for training and 60% (n = 240) for testing, with 5-fold cross-validation. The automated system achieved robust performance metrics across 240 test instances: overall accuracy of 83.0%, precision of 85.0%, recall of 87.0%, and F1-score of 86.0%. Confusion matrix analysis revealed true positives (n = 130), true negatives (n = 70), false positives (n = 22), and false negatives (n = 18). Individual case analysis demonstrated 80% agreement with ground truth classifications. System performance significantly exceeded the random classification baseline (p < 0.001). The strategic extreme player tracking approach achieved substantial computational efficiency while maintaining decision accuracy across diverse match scenarios. A YOLOv8-based offside detection system demonstrates proof-of-concept feasibility for decision-support applications in football officiating. Performance (83% accuracy, 95% CI: 79-87%) on 240 test instances remains below professional benchmarks (> 93%), with single-camera constraints and limited dataset size restricting generalizability to proof-of-concept validation rather than operational deployment.
- Yüksek Lisans TeziAçık Erişim2025Yozgat ilinde yerel medya ve yapay zeka: Nitel bir analiz
Rabia Sultan Sedef
Bu çalışma Yozgat ilindeki yerel medya çalışanlarının yapay zekâya yönelik algıları ve yapay zekânın haber üretimindeki yerini tespit etmek amacıyla nitel araştırma yöntemi ile yapılmıştır. Bu çalışma, Şubat 2025 ile Mayıs 2025 tarihleri arasında gerçekleştirilen yüz yüze görüşmelerle yürütülmüştür. Araştırmada nitel araştırma yöntemi benimsenmiş olup, veri toplama aracı olarak yarı yapılandırılmış görüşme tekniği kullanılmıştır. Çalışma kapsamında, katılımcılara yapay zeka ile ilgili düşünceleri ve kullanım alanları üzerine sorular yöneltilmiştir. Elde edilen veriler için nitel araştırma yöntemi çerçevesinde yer alan veri analizi tekniklerinden biri olan betimsel analiz kullanılmıştır. Katılımcılar, 8 kadın ve 22 erkek olmak üzere toplamda 30 kişiden oluşmaktadır. Katılımcılar, yapay zekânın yalnızca avantajlarının değil, aynı zamanda birtakım dezavantajlarının da bulunduğunu ifade etmişlerdir. Olumlu yanları ile kullanıldığında yapay zekânın meslekleri açısından bir yardımcı görevi gördüğünü söylemişlerdir.
- Yüksek Lisans TeziAçık Erişim2025Otomatik avuç içi bölütlenmesinin gerçekleştirilmesinde farklı derin öğrenme modellerinin performanslarının incelenmesi
Kadir Yalçın
Tıbbi görüntüleme teknolojilerindeki ilerlemeler, özellikle derin öğrenme tabanlı yöntemlerin kullanımıyla, el bölütleme gibi spesifik görevlerde dikkat çekici başarılar elde etmiştir. El bölütleme, biyometrik doğrulama sistemlerinde kimlik tespiti için kritik bir öneme sahiptir. Avuç içi damar yapıları, parmak uzunlukları ve deri dokusu gibi benzersiz biyometrik özelliklerin doğru bir şekilde çıkarılması, el bölütleme algoritmalarının doğruluğuna bağlıdır. Bu tez çalışmasında, el bölütleme probleminin çözümü için oto kodlayıcı tabanlı U-Net, MA-Net, FPN ve PSP-Net modelleri, Vision Transformer, EfficientNet-B5 ve ResNet-34 omurgaları ile değerlendirilmiştir. Modeller, Doğruluk (ACC), Intersection over Union (IoU), Dice katsayısı ve F1 skor gibi metriklerle analiz edilmiştir. Sonuçlar, Vision Transformer ile entegre edilen U-Net modelinin bölütleme de en yüksek başarı sağladığını göstermiştir (IoU: 0.8917, Dice: 0.9892). MA-Net, ResNet-34 omurgasıyla kullanıldığında en yüksek doğruluk değerine (0.9921) ulaşmıştır. Ayrıca bu kombinasyon, en yüksek F1 skorunu (0.9584) da elde etmiştir. EfficientNet-B5, MA-Net ve FPN ile etkili sonuçlar sunmuştur. Çalışmanın sonuçları, derin öğrenme modelleriyle elde edilen bölütleme başarısının biyometrik doğrulama sistemlerinin güvenilirliğini artırabileceğini ortaya koymaktadır. Özellikle U-Net - Vision Transformer ve MA-Net – ResNet-34 kombinasyonları, elin bölütlenmesinde yüksek performans göstermiştir. Bu bulgular, el bölütleme uygulamalarında model ve omurga seçimlerinin kritik olduğunu vurgulamaktadır.
- MakaleAçık Erişim2025Scopus SJRQ1Towards precision dentistry through artificial intelligence and blockchain-based digital Twins: Investigating challenges and solution strategies
Erol, Ismail; Peker, Iskender; Medeni, Ihsan Tolga; Yuce, Fatma
The integration of artificial intelligence (AI), blockchain, and digital twins (DT) in dentistry is essential to meet evolving patient expectations and global healthcare challenges. However, realizing their full potential requires addressing significant adoption barriers. While conceptual studies have explored challenges to implementing AI, blockchain, and DT in dentistry, no prior research has systematically analyzed the interrelationships among these obstacles using a structured decision framework. This study employs pentagonal fuzzy (PF) DEMATEL to investigate the connections among challenges to effectively implementing AI and blockchain-based DT in dentistry. Expert opinions highlight ethical considerations and bias and fairness in AI-based DT as the most critical causal challenges. We propose an AI and blockchain-based DT architecture that enables human doctors to leverage DT-based decision support, while AI-driven systems execute high-precision procedures with blockchain-verified workflows. This framework enhances treatment accuracy and personalization in dentistry and offers a scalable model for addressing societal concerns, such as privacy, fairness, and labor dynamics, in technology-driven ecosystems. A pilot study framework is proposed to validate diagnostic accuracy, data security, and treatment customization. Accompanied by stakeholder guidelines, this research bridges dentistry with broader discussions on technology's societal impact, emphasizing the need for interdisciplinary strategies to ensure equitable and trustworthy innovation.
AtıfScopus4WoS3OpenAlex5İncele - MakaleAçık Erişim2025Scopus SJRQ1Adoption of AI-based order picking in warehouse: benefits, challenges, and critical success factors
Rad, Fakhreddin Fakhrai; Oghazi, Pejvak; Onur, İzmir; Kordestani, Arash
This research assesses the adoption of artificial intelligence (AI)-based batch order picking in a warehouse, focusing on benefits, challenges, and critical success factors. Using customer order data and simulation, the study employs a quantitative approach, combining mathematical and statistical estimations with qualitative examinations centered on interviews with the warehouse staff, its Enterprise Resource Planning (ERP) developer, and the AI developer. The findings of this mixed-method study reveal that the AI-based order-picking system (AI-based system) has improved order-picking efficiency by reducing travel distance and time. Nevertheless, challenges hinder maximum utilization of the system. In addition, the research highlights critical success factors and other benefits of adopting the system tailored to warehouse management. Understanding the lessons learned in this research is essential for businesses seeking to adopt AI to enhance their efficiency.
AtıfScopus13WoS5OpenAlex5İncele - BildiriYalnız künye2025Hybrid CNN-LSTM Framework for Automated Detection of Pediatric Heart Murmurs
Al-Taib, Asmaa Abduh; Ture, Hayati; Aslan, Muhammed Emin
This study presents a comprehensive deep learning framework for automated heart murmur detection from phonocardiogram (PCG) signals to address the subjective limitations of cardiac auscultation. Using the CirCor DigiScope dataset of 5,272 pediatric heart sound recordings from 1,568 subjects, we systematically evaluated multiple approaches including traditional machine learning models (Logistic Regression, SVM, KNN, Decision Tree, AdaBoost), basic deep learning architectures (standalone CNN and LSTM), and advanced transfer learning models (DenseNet121, EfficientNet B0/B1/B2, ResNet 50/101, MobileNet V2/V3, GhostNet, AttentionCNN). Our hybrid CNN-LSTM architecture combines mel-spectrogram spatial feature extraction with temporal sequence modeling using 128 traditional audio features and comprehensive preprocessing with data augmentation. The CNN-LSTM model achieved superior performance with 84.6% accuracy, 0.84 F1-score, 0.748 precision, and 0.800 recall, significantly outperforming traditional methods (best SVM: 0.716 F1-score) and advanced architectures (best DenseNet121: 0.72 F1-score), demonstrating robust classification with 0.879 AUC. Results indicate that hybrid deep learning architectures effectively automate cardiac auscultation, providing objective diagnostic tools with potential for improved healthcare accessibility and clinical decision-making.
- MakaleAçık Erişim2025Scopus SJRQ1Evaluation of ChatGPT-4, Gemini, Claude, and Copilot in Generating Nursing Diagnoses Based on NANDA-I Taxonomy II: A Comparative Cross-Sectional Study
Tuncer, Metin; Yalcinkaya, Turgay
Aim: To evaluate the capability of large language models to generate nursing diagnoses based on NANDA-I Taxonomy II and assess their performance across domains and overall. Background: Large language models are emerging tools in nursing, showing potential to aid in diagnosis generation and education. However, their accuracy and applicability in clinical and educational settings remain underexplored. Methods: This cross-sectional comparative study used 10 realistic patient scenarios based on NANDA-I Taxonomy II, covering 12 domains. The study aimed to evaluate the capability of four models to generate nursing diagnoses based on patient scenarios. The responses were assessed by five nursing experts for accuracy and alignment with NANDA-I Taxonomy II in a single-blind evaluation process. Results: All models demonstrated similar performance across different domains and overall, with Claude attaining the highest overall performance score. Expert evaluations indicated moderate interrater reliability. Discussion: Small variations between models and occasional omissions suggest that expert review is still required before clinical use. Conclusions: Large language models are not yet sufficiently reliable for independent use in clinical settings and nursing education. Their application as supportive tools necessitates a cautious approach. Moreover, the development of specialized models designed to address the unique demands of the nursing field would be advantageous. Implications for nursing: When large language models are used in nursing practice, their limitations should be considered, and the outputs they produce should be verified by nurses. Implications for nursing policy: Ensuring the safe integration of artificial intelligence tools into nursing necessitates the establishment of robust regulatory policies to safeguard patient safety, the deployment of effective systems to monitor models’ performance, and the development of comprehensive guidelines and training programs.
AtıfScopus2WoS1OpenAlex1İncele - MakaleYalnız künye2025Scopus SJRQ1ChatGPT-4 in Nursing Research: A Methodological Evaluation of Bias Risk in Randomized Controlled Trials
Tuncer, Metin; Tuncer, Gülsüm Zekiye
Background: Conducting bias assessments in systematic reviews is a time-consuming process that involves subjective judgments. The use of artificial intelligence (AI) technologies to perform these assessments can potentially save time and enhance consistency. Nevertheless, the efficacy of AI technologies in conducting bias assessments remains inadequately explored. Aim: This study aims to evaluate the efficacy of ChatGPT-4o in assessing bias using the revised Cochrane RoB2 tool, focusing on randomized controlled trials in nursing. Methods: ChatGPT-4o was provided with the RoB2 assessment guide in the form of a PDF document and instructed to perform bias assessments for the 80 open-access RCTs included in the study. The results of the bias assessments conducted by ChatGPT-4o for each domain were then compared with those of the meta-analysis authors using Cohen's weighted kappa analysis. Results: Weighted Cohen's kappa values showed better agreement in bias in the measurement of the outcome (D4, 0.22) and bias arising from the randomization process (D1, 0.20), while negative values in bias due to missing outcome data (D3, −0.12) and bias in the selection of the reported result (D5, −0.09) indicated poor agreement. The highest accuracy was observed in D5 (0.81), and the lowest in D1 (0.60). F1 scores were highest in bias due to deviations from intended interventions (D2, 0.74) and lowest in D3 (0.00) and D5 (0.00). Specificity was higher in D5 (0.93) and D3 (0.82), while sensitivity and precision were low in these domains. Conclusions: The agreement between ChatGPT-4o and the meta-analysis studies in the same RCT assessments is generally low. This indicates that ChatGPT-4o requires substantial enhancements before it can be used as a reliable tool for bias risk assessments. Clinical Relevance: The AI–based tools have the potential to expedite bias assessment in systematic reviews. However, this study demonstrates that ChatGPT-4o, in its current form, lacks sufficient consistency, indicating that such tools should be integrated cautiously and used under continuous human oversight, particularly in evidence-based evaluations that inform clinical decision-making.
AtıfScopus1İncele - MakaleAçık Erişim2024EVALUATION OF CONSUMERS’ USE OF SMART ROBOTIC VACUUM CLEANERS UNDER EXTENDED EXPECTATIONCONFIRMATION MODEL; [VREDNOVANJE PROVEDENO MEĐU POTROŠAČIMA PAMETNIH ROBOTSKIH USISAVAČA U OKVIRU PROŠIRENOGA MODELA POTVRDE OČEKIVANJA]
Avci, Ibrahim; Kocan, Mahmut; Kirmizibiber, Ahmet
Purpose – The present research study extends the Expectation-Confirmation Model (ECM) by focusing on satisfaction, continuance intention, and recommendation intention among smart robotic vacuum cleaner users. The impacts of battery life concern and perceived privacy factors on satisfaction and continuance intention of the smart robotic vacuum cleaner were investigated by adding the variables of battery life concern, perceived privacy, and recommendation intention in the ECM. Design/methodology/approach – The participants of this study consisted of smart robotic vacuum cleaner users in Turkey. The snowball sampling method, as one of the non-random sampling methods, was used to reach the participants; data was collected using an online survey created with Google Forms, asking the participants to share the survey link with people around them who have smart robotic vacuum cleaners. A total of 218 smart robotic vacuum cleaner users participated in the study between March 1 and May 1, 2023. Findings and implications – Structural equation modeling (SEM) was used as the data analysis technique in the study. While the ECM was confirmed as a result of the SEM analysis, an important finding was that consumers with battery life concerns intend to continue using the smart robotic vacuum cleaner despite a negative relationship between battery life concern and satisfaction. While the effect of perceived privacy on continuance intention was not significant, satisfaction was found to have a significant effect on continuance intention. Given that rtificial intelligence-related businesses face fierce competition, organizations that wish to prosper in this environment must excel in a variety of areas, ranging from product design to marketing and from sales policies to post-sales assistance. Considering the findings, determining the factors that affect the satisfaction and intention to continue using robot vacuum cleaners will be a guide for smart robot vacuum cleaner manufacturers and marketers. At the same time, the fact that the issue has been investigated within the framework of a model will also make significant contributions to the literature. Limitations – Different factors influence smart robotic vacuum cleaner users’ satisfaction and desire to continue using them, but only battery-life concerns and perceived privacy variables were incorporated into the ECM model in this research study. Another limitation is connected to the research sample. Because the snowball sampling method was employed to obtain data for the research, its findings cannot be generalized as they cover only smart robotic vacuum cleaner users who participated in the survey. Originality/value – The ECM employed in the study was expanded by including the variables of battery-life concern, perceived privacy, and recommendation intention. A general lack of information among potential consumers regarding artificial intelligence and smart robotic vacuum cleaners, which is highly fascinating and a source of curiosity, as well as the lack of research on this issue in the literature, underscore the significance of this research study. By integrating and expanding the ECM for application to consumers using smart robotic vacuum cleaners, this study adds a fresh viewpoint to the literature.
AtıfOpenAlex1İncele - MakaleAçık Erişim2024Scopus SJRQ2Investigation of nurses’ general attitudes toward artificial intelligence and their perceptions of ChatGPT usage and influencing factors
Tuncer, Gülsüm Zekiye; Tuncer, Metin
Purpose: This study aimed to investigate professional nurses’ general attitudes toward artificial intelligence, their knowledge and perceptions of ChatGPT usage, and the influencing factors. Methods: The population of the research consists of nurses who follow a social media platform account in Turkey. The sample of the study consisted of 288 nurses who participated in the study between December 2023 and March 2024. Data were collected through an account on a social media platform via Google Forms using the Information Identification Questionnaire for ChatGPT and Artificial Intelligence Programs and the General Attitudes to Artificial Intelligence Scale (GAAIS). Results: The mean scores obtained from the overall GAAIS and its Positive Attitudes subscale from the participants in this study were 67.54 ± 13.14 and 41.89 ± 11.24, respectively. Of the participants, 48.3% knew about ChatGPT and artificial intelligence programs. Of the participants, 27.8% used ChatGPT and artificial intelligence programs. Their scores for the Positive Attitude subscale were higher than were the scores of those who did not use such programs. Of the participants, 84.4% thought that nurses should be made aware of ChatGPT and artificial intelligence programs, 67% thought that the use of these programs would contribute to nurses’ professional development, 42.4% thought that the use of these programs would not reduce nurses’ workload, and 58.3% thought that the use of these programs would positively affect patient care. Conclusion: In this study, it can be said that nurses in Turkey have positive attitudes toward integrating ChatGPT and AI programs to improve patient outcomes and add them to nursing practices. Implications for nursing practice: The present study in which nurses’ attitudes toward the implementation of ChatGPT and artificial intelligence programs were investigated is expected to provide information for healthcare institutions, policy makers and artificial intelligence developers on the integration of ChatGPT and artificial intelligence into nursing practice. It is necessary to create environments that use AI technologies that reduce the nursing workload of nurses in the clinical area and positively affect the quality of patient care.
AtıfScopus47WoS47OpenAlex46İncele - MakaleYalnız künye2023Scopus SJRQ1Detection of forest fire using deep convolutional neural networks with transfer learning approach[Formula presented]
Reis, Hatice Catal; Turk, Veysel
Forest fires caused by natural causes such as climate change, temperature increase, lightning strikes, volcanic activity or human effects are among the world's most dangerous, deadly, and destructive disasters. Detection, prevention, and extinguishing forest fires is challenging. In addition, forest fires can cause habitat destruction that cannot be controlled in time and cause great material and moral losses. Therefore, fast and accurate Detection of forest fires is vital in emergency response. Here, in solving the problem, the transfer learning method from deep learning sub-topics can be used, which allows the application of pre-trained networks to a new problem. The Fire Luminosity Airborne-based Machine learning Evaluation dataset (consisting of forest fire images) obtained by Unmanned Aerial Vehicle was used in this study. In the Detection of forest fire images in the dataset, InceptionV3, DenseNet121, ResNet50V2, NASNetMobile, VGG-19 deep learning algorithms, transfer learning techniques that can produce more successful results than networks trained from scratch, and hybrid proposed with Support Vector Machine, Random Forest, Bidirectional Long Short-Term Memory, Gated Recurrent Unit algorithms methods have been applied. In the classification study with the Fire Luminosity Airborne-based Machine learning Evaluation dataset in performance measurement, 97.95% accuracy was obtained from the DenseNet121 model, which was started with random weights. In the transfer learning study using ImageNet weights, satisfactory results were obtained with 99.32% accuracy in the DenseNet121 model. We anticipate that working in forest fire detection and response can be entirely satisfactory.
AtıfScopus87WoS64OpenAlex92İncele
