PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1928

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Determining the Effect of Video Information on the Dental Anxiety Levels of the Endodontic Patients: a Randomised Clinical Trial
(Wiley, 2025) Anatürk, Şule; Dönmez Özkan, Hicran; Saral, İlkim Pınar; Çakar, Tuna
Objective The present study assessed the effectiveness of pretreatment education in the form of Visual Video Information (VVI) on the anxiety levels of patients during endodontic treatment steps. Methods Patients (n = 120) having single-rooted teeth with a single root canal diagnosed with asymptomatic irreversible pulpitis and/or pre-prosthetic root canal treatment were included in this study. After completing anxiety scales and a sociodemographic/dental habits survey, the patients were randomly divided into two groups. Just before the endodontic treatment, VVI was given to the video group patients, while the control group patients received routine information verbally. In both groups, a galvanic skin response (GSR) device was placed on the patients' wrists to record the stress levels during the endodontic treatment process. Anxiety scales and a feedback-satisfaction survey were administered to all patients after the treatment process. Then, statistical analysis was performed (alpha = 0.05). Results This study performed 60 endodontic treatments on 60 patients (30 females and 30 males). Sociodemographic characteristics and dental treatment habits of the patients significantly affected dental anxiety scale scores (p < 0.05). VVI resulted in a significant decrease in the mean scores of anxiety before and after the treatment, but this decrease was not significant between the groups (p > 0.05). Similarly, VVI did not impact the GSR readings between the groups during treatment (p > 0.05). Conclusions The educational VVI is effective for reducing anxiety in patients undergoing endodontic treatment. In addition, the electrodermal activity method is a promising alternative for objectively assessing anxiety levels.
Citation - WoS: 5
Citation - Scopus: 5
Physicians’ Ethical Concerns About Artificial Intelligence in Medicine: a Qualitative Study: “the Final Decision Should Rest With a Human”
(Frontiers Media SA, 2024) Kahraman, F.; Aktas, A.; Bayrakceken, S.; Çakar, T.; Tarcan, H.S.; Bayram, B.; Ulman, Y.I.
Background/aim: Artificial Intelligence (AI) is the capability of computational systems to perform tasks that require human-like cognitive functions, such as reasoning, learning, and decision-making. Unlike human intelligence, AI does not involve sentience or consciousness but focuses on data processing, pattern recognition, and prediction through algorithms and learned experiences. In healthcare including neuroscience, AI is valuable for improving prevention, diagnosis, prognosis, and surveillance. Methods: This qualitative study aimed to investigate the acceptability of AI in Medicine (AIIM) and to elucidate any technical and scientific, as well as social and ethical issues involved. Twenty-five doctors from various specialties were carefully interviewed regarding their views, experience, knowledge, and attitude toward AI in healthcare. Results: Content analysis confirmed the key ethical principles involved: confidentiality, beneficence, and non-maleficence. Honesty was the least invoked principle. A thematic analysis established four salient topic areas, i.e., advantages, risks, restrictions, and precautions. Alongside the advantages, there were many limitations and risks. The study revealed a perceived need for precautions to be embedded in healthcare policies to counter the risks discussed. These precautions need to be multi-dimensional. Conclusion: The authors conclude that AI should be rationally guided, function transparently, and produce impartial results. It should assist human healthcare professionals collaboratively. This kind of AI will permit fairer, more innovative healthcare which benefits patients and society whilst preserving human dignity. It can foster accuracy and precision in medical practice and reduce the workload by assisting physicians during clinical tasks. AIIM that functions transparently and respects the public interest can be an inspiring scientific innovation for humanity. Copyright © 2024 Kahraman, Aktas, Bayrakceken, Çakar, Tarcan, Bayram, Durak and Ulman.
Citation - WoS: 3
Citation - Scopus: 4
Unraveling Neural Pathways of Political Engagement: Bridging Neuromarketing and Political Science for Understanding Voter Behavior and Political Leader Perception
(2023) Çakar, Tuna; Filiz, Gözde
Political neuromarketing is an interdisciplinary field that combines marketing, neuroscience, and psychology to understand voter behavior and political leader perception. This interdisciplinary field offers novel techniques to understand complex phenomena such as voter engagement, political leadership, and party branding. This study aims to understand the neural activation patterns of voters when they are exposed to political leaders using functional near-infrared spectroscopy (fNIRS) and machine learning methods. We recruited participants and recorded their brain activity using fNIRS when they were exposed to images of different political leaders. This neuroimaging method (fNIRS) reveals brain regions central to brand perception, including the dorsolateral prefrontal cortex (dlPFC), the dorsomedial prefrontal cortex (dmPFC), and the ventromedial prefrontal cortex (vmPFC). Machine learning methods were used to predict the participants' perceptions of leaders based on their brain activity. The study has identified the brain regions that are involved in processing political stimuli and making judgments about political leaders. Within this study, the best-performing machine learning model, LightGBM, achieved a highest accuracy score of 0.78, underscoring its efficacy in predicting voters' perceptions of political leaders based on the brain activity of the former. The findings from this study provide new insights into the neural basis of political decision-making and the development of effective political marketing campaigns while bridging neuromarketing, political science and machine learning, in turn enabling predictive insights into voter preferences and behavior

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