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Chat Generative Pre-Trained Transformer (ChatGPT) is a sophisticated natural language model that employs advanced deep learning techniques and is trained on extensive datasets to produce responses akin to human conversation for user inputs. In this study, ChatGPT's success in the Turkish Neurosurgical Society Proficiency Board Exams (TNSPBE) is compared to the actual candidates who took the exam, along with identifying the types of questions it answered incorrectly, assessing the quality of its responses, and evaluating its performance based on the difficulty level of the questions. Scores of all 260 candidates were recalculated according to the exams they took and included questions in those exams for ranking purposes of this study. The average score of the candidates for a total of 523 questions is 62.02 ± 0.61 compared to ChatGPT, which was 78.77. We have concluded that in addition to ChatGPT's higher response rate, there was also a correlation with the increase in clarity regardless of the difficulty level of the questions with Clarity 1.5, 2.0, 2.5, and 3.0. In the participants, however, there is no such increase in parallel with the increase in clarity.
Assuntos
Inteligência Artificial , Avaliação Educacional , Idioma , Neurocirurgia , Neurocirurgia/educaçãoRESUMO
BACKGROUND: Vestibular schwannomas (VSs) are benign tumors often monitored over time, with measurement techniques for assessing growth rates subject to significant interobserver variability. Automatic segmentation of these tumors could provide a more reliable and efficient for tracking their progression, especially given the irregular shape and growth patterns of VS. METHODS: Various studies and segmentation techniques employing different Convolutional Neural Network architectures and models, such as U-Net and convolutional-attention transformer segmentation, were analyzed. Models were evaluated based on their performance across diverse datasets, and challenges, including domain shift and data sharing, were scrutinized. RESULTS: Automatic segmentation methods offer a promising alternative to conventional measurement techniques, offering potential benefits in precision and efficiency. However, these methods are not without challenges, notably the "domain shift" that occurs when models trained on specific datasets underperform when applied to different datasets. Techniques such as domain adaptation, domain generalization, and data diversity were discussed as potential solutions. CONCLUSIONS: Accurate measurement of VS growth is a complex process, with volumetric analysis currently appearing more reliable than linear measurements. Automatic segmentation, despite its challenges, offers a promising avenue for future investigation. Robust well-generalized models could potentially improve the efficiency of tracking tumor growth, thereby augmenting clinical decision-making. Further work needs to be done to develop more robust models, address the domain shift, and enable secure data sharing for wider applicability.
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BACKGROUND: The aim of this paper is to report one of the significant applications of artificial intelligence (AI) and how it affects everyday clinical practice in neurosurgery. The authors present a case in which a patient was diagnosed via an AI algorithm during ongoing magnetic resonance imaging (MRI). According to this algorithm, the corresponding physicians were immediately warned, and the patient received prompt appropriate treatment. OBSERVATIONS: A 46-year-old female presenting with nonspecific headache was admitted to undergo MRI. Scanning revealed an intraparenchymal mass that was detected by an AI algorithm running on real-time patient data while the patient was still in the MRI scanner. The day after MRI, a stereotactic biopsy was performed. The pathology report confirmed an isocitrate dehydrogenase wild-type diffuse glioma. The patient was referred to the oncology department for evaluation and immediate treatment. LESSONS: This is the first report of a glioma diagnosed by an AI algorithm and a subsequent prompt operation in the literature-the first of many and an example of how AI will enhance clinical practice.