Bilgisayar Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940
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Article Citation - WoS: 16Citation - Scopus: 20Physicians’ Ethical Concerns About Artificial Intelligence in Medicine: a Qualitative Study: “the Final Decision Should Rest With a Human”(Frontiers Media SA, 2024-11-27) 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.Article Citation - WoS: 6Citation - Scopus: 8Designing restorative landscapes for students: A Kansei engineering approach enhanced by VR and EEG technologies(Elsevier, 2024-09-01) Karaca, Elif; Çakar, Tuna; Karaca, Mehmet; Gul, Hasan Huseyin Mirac; Hüseyin Miraç Gül, HasanThis study explores the alignment of specific landscape features within school environments with the core elements of Attention Restoration Theory (ART) that includes Coherence, Fascination, Compatibility, and Being Away. Utilizing Kansei Engineering, this research integrates emotional analysis into landscape design by employing Virtual Reality (VR) and Electroencephalogram (EEG) technologies to record students' responses to different landscape simulations. Analytical techniques, including the Taguchi Method and Analysis of Variance (ANOVA), were applied to evaluate the data. The findings have revealed that students associate a sense of enclosure with a coherent landscape and openness with a fascinating landscape, the lawn's significance was also highlighted for coherent landscape. However, limited insights were gained regarding Compatibility and Being Away. The study advocates for diverse cognitive zones within school landscapes to promote mental restoration, emphasizing the need for varied design elements that cater to the elevated experience of students.Article Citation - WoS: 2Citation - Scopus: 2Warning Notes in a Learner’s Dictionary: a Study of the Effectiveness of Different Formats(International Journal of Lexicography, 2022-01-25) Çakar, Tuna; Nesi, Hilary; Nural, ŞükrüThis study used an online correction task to explore the extent to which different types of warning notes in Longman Dictionary of Contemporary English Online were heeded when users tried to correct errors in the use of L2 target words. The task was completed by 332 participants, yielding 1,819 answers produced after clicking on links to relevant entries. Warning notes were categorised in terms of their formatting features, but there were found to be inconsistencies in the way the dictionary associated different categories with different kinds of learner error. Participants judged warning notes with more visual enhancements to be more useful, but in the correction task the position of the warning notes also seemed to affect the degree to which the warnings were successfully applied. Different types of warning notes in learners’ dictionaries have not been examined previously in any depth, and the results suggest that some adjustments to formatting and placement might make them more effective.