AIMC Topic: Turkey

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Leveraging large language models to mimic domain expert labeling in unstructured text-based electronic healthcare records in non-english languages.

BMC medical informatics and decision making
BACKGROUND: The integration of big data and artificial intelligence (AI) in healthcare, particularly through the analysis of electronic health records (EHR), presents significant opportunities for improving diagnostic accuracy and patient outcomes. H...

Psychometric properties and Turkish adaptation of the artificial intelligence attitude scale (AIAS-4): evidence for construct validity.

BMC psychology
Artificial intelligence (AI) attitude scales can be used to better evaluate the benefit and drawback cons of AI. This article consists of two different studies examining attitudes towards AI. In Study I (N = 370), the four-item Artificial Intelligenc...

Automatic bone age assessment: a Turkish population study.

Diagnostic and interventional radiology (Ankara, Turkey)
PURPOSE: Established methods for bone age assessment (BAA), such as the Greulich and Pyle atlas, suffer from variability due to population differences and observer discrepancies. Although automated BAA offers speed and consistency, limited research e...

Exploring the role of artificial intelligence in Turkish orthopedic progression exams.

Acta orthopaedica et traumatologica turcica
OBJECTIVE: The aim of this study was to evaluate and compare the performance of the artificial intelligence (AI) models ChatGPT-3.5, ChatGPT-4, and Gemini on the Turkish Specialization Training and Development Examination (UEGS) to determine their ut...

Predicting total healthcare demand using machine learning: separate and combined analysis of predisposing, enabling, and need factors.

BMC health services research
OBJECTIVE: Predicting healthcare demand is essential for effective resource allocation and planning. This study applies Andersen's Behavioral Model of Health Services Use, focusing on predisposing, enabling, and need factors, using data from the 2022...

Deep learning for named entity recognition in Turkish radiology reports.

Diagnostic and interventional radiology (Ankara, Turkey)
PURPOSE: The primary objective of this research is to enhance the accuracy and efficiency of information extraction from radiology reports. In addressing this objective, the study aims to develop and evaluate a deep learning framework for named entit...

Comparison of different dental age estimation methods with deep learning: Willems, Cameriere-European, London Atlas.

International journal of legal medicine
This study aimed to compare dental age estimates using Willems, Cameriere-Europe, London Atlas, and deep learning methods on panoramic radiographs of Turkish children. The dental ages of 1169 children (613 girls, 556 boys) who agreed to participate i...

BiFPN-enhanced SwinDAT-based cherry variety classification with YOLOv8.

Scientific reports
Accurate classification of cherry varieties is crucial for their economic value and market differentiation, yet their genetic diversity and visual similarity make manual identification challenging, hindering efficient agricultural and trade practices...

Performance of artificial intelligence on Turkish dental specialization exam: can ChatGPT-4.0 and gemini advanced achieve comparable results to humans?

BMC medical education
BACKGROUND: AI-powered chatbots have spread to various fields including dental education and clinical assistance to treatment planning. The aim of this study is to assess and compare leading AI-powered chatbot performances in dental specialization ex...

ChatGPT-4 Omni's superiority in answering multiple-choice oral radiology questions.

BMC oral health
OBJECTIVES: This study evaluates and compares the performance of ChatGPT-3.5, ChatGPT-4 Omni (4o), Google Bard, and Microsoft Copilot in responding to text-based multiple-choice questions related to oral radiology, as featured in the Dental Specialty...