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(1) Background: Language represents a crucial ability of humans, enabling communication and collaboration. ChatGPT is an AI chatbot utilizing the GPT (Generative Pretrained Transformer) language model architecture, enabling the generation of human-like text. The aim of the research was to assess the effectiveness of ChatGPT-3.5 and the latest version, ChatGPT-4, in responding to questions posed within the scope of a periodontology specialization exam. (2) Methods: Two certification examinations in periodontology, available in both English and Polish, comprising 120 multiple-choice questions, each in a single-best-answer format. The questions were additionally assigned to five types in accordance with the subject covered. These exams were utilized to evaluate the performance of ChatGPT-3.5 and ChatGPT-4. Logistic regression models were used to estimate the chances of correct answers regarding the type of question, exam session, AI model, and difficulty index. (3) Results: The percentages of correct answers obtained by ChatGPT-3.5 and ChatGPT-4 in the Spring 2023 session in Polish and English were 40.3% vs. 55.5% and 45.4% vs. 68.9%, respectively. The periodontology specialty examination test accuracy of ChatGPT-4 was significantly better than that of ChatGPT-3.5 for both sessions (p < 0.05). For the ChatGPT-4 spring session, it was significantly more effective in the English language (p = 0.0325) due to the lack of statistically significant differences for ChatGPT-3.5. In the case of ChatGPT-3.5 and ChatGPT-4, incorrect responses showed notably lower difficulty index values during the Spring 2023 session in English and Polish (p < 0.05). (4) Conclusions: ChatGPT-4 exceeded the 60% threshold and passed the examination in the Spring 2023 session in the English version. In general, ChatGPT-4 performed better than ChatGPT-3.5, achieving significantly better results in the Spring 2023 test in the Polish and English versions.
(1) Background: Language represents a crucial ability of humans, enabling communication and collaboration. ChatGPT is an AI chatbot utilizing the GPT (Generative Pretrained Transformer) language model architecture, enabling the generation of human-like text. The aim of the research was to assess the effectiveness of ChatGPT-3.5 and the latest version, ChatGPT-4, in responding to questions posed within the scope of a periodontology specialization exam. (2) Methods: Two certification examinations in periodontology, available in both English and Polish, comprising 120 multiple-choice questions, each in a single-best-answer format. The questions were additionally assigned to five types in accordance with the subject covered. These exams were utilized to evaluate the performance of ChatGPT-3.5 and ChatGPT-4. Logistic regression models were used to estimate the chances of correct answers regarding the type of question, exam session, AI model, and difficulty index. (3) Results: The percentages of correct answers obtained by ChatGPT-3.5 and ChatGPT-4 in the Spring 2023 session in Polish and English were 40.3% vs. 55.5% and 45.4% vs. 68.9%, respectively. The periodontology specialty examination test accuracy of ChatGPT-4 was significantly better than that of ChatGPT-3.5 for both sessions (p < 0.05). For the ChatGPT-4 spring session, it was significantly more effective in the English language (p = 0.0325) due to the lack of statistically significant differences for ChatGPT-3.5. In the case of ChatGPT-3.5 and ChatGPT-4, incorrect responses showed notably lower difficulty index values during the Spring 2023 session in English and Polish (p < 0.05). (4) Conclusions: ChatGPT-4 exceeded the 60% threshold and passed the examination in the Spring 2023 session in the English version. In general, ChatGPT-4 performed better than ChatGPT-3.5, achieving significantly better results in the Spring 2023 test in the Polish and English versions.
Over recent decades, machine learning has been widely implemented in medical radiology. Radiologists, who are at the forefront of clinical practice, need to be aware of the benefits of machine learning to facilitate its implementation. It is crucial for them to thoroughly understand and effectively integrate machine learning into the practical realm of medical radiology. In this review, we highlight the principles and applications of machine learning in medical radiology and provide a summary of its development in this field. Machine learning has significantly advanced diagnostic imaging, enhancing detection, segmentation, and image reconstruction, while improving workflow efficiency and radiology reporting. Current literature indicates three primary challenges in implementing machine learning: data standardization, validation of model performance, and regulatory compliance. The successful integration of machine learning in clinical practice requires robust data security protocols and clear frameworks for professional accountability. To prepare for this technological transition, radiologists must develop new competencies through enhanced educational programs and adapt their roles to focus more on clinical decision-making and multidisciplinary collaboration while leveraging machine learning as a supportive tool.
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