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Introduction: The development of Artificial Intelligence (AI) and attempts to use it in medicine are increasingly becoming the subject of more scientific research. Aim: The aim of this article is to present the effectiveness of the advanced language model, ChatGPT-3.5 in the context of the pass rate of the Polish National Specialist Examination (PES) in allergology. Additionally, it seeks to comprehend the potential applications of artificial intelligence in the field of medicine, particularly within allergology. Material and methods: The study used the latest available PES exam prepared by the Medical Research Centre in Lodz. 118 questions were asked using the openai.com platform, which allows free access to the ChatGPT-3.5 model. All questions were classified according to Bloom's taxonomy to assess their complexity and difficulty, with additional three categorisations. Each question was asked five times. Results: ChatGPT-3.5 did not pass the allergology PES, achieving a score of 52.54%. It was observed that the model performed better in answering memory questions (60%) compared to those requiring comprehension and critical thinking, where the results were slightly lower (45%). Moreover, within the categories of 'treatment', 'immune system' and 'symptoms', the model exceeded the passing threshold. Questions to which ChatGPT provided the correct answer significantly exhibited higher difficulty compared to those to which it provided an incorrect response. Conclusions: The results indicate that ChatGPT's pass rate in the allergology PES is considerably lower than that of resident doctors specializing in this field. The potential applications of AI in medicine require further research to effectively support clinical practice among physicians. [ABSTRACT FROM AUTHOR]

Introduction: The rapid development of artificial intelligence (AI) has sparked a desire to analyse its potential applications in medicine. The aim of this article is to present the effectiveness of the ChatGPT advanced language model in the context of the pass rate of the polish National Specialty Examination (PES) in nuclear medicine. It also aims to identify its strengths and limitations through an in-depth analysis of the issues raised in the exam questions. Methods: The PES exam provided by the Centre for Medical Examinations in Łódź, consisting of 120 questions, was used for the study. The questions were asked using the openai.com platform, through which free access to the GPT-3.5 model is available. All questions were classified according to Bloom's taxonomy to determine their complexity and difficulty, and according to two authors' subcategories. To assess the model's confidence in the validity of the answers, each questions was asked five times in independent sessions. Results: ChatGPT achieved 56%, which means it did not pass the exam. The pass rate is 60%. Of the 117 questions asked, 66 were answered correctly. In the percentage of each type and subtype of questions answered correctly, there were no statistically significant differences. Conclusion: Further testing is needed using the questions provided by Centre for Medical Examinations from the nuclear medicine specialty exam to evaluate the utility of the ChatGPT model. This opens the door for further research on upcoming improved versions of the ChatGPT. [ABSTRACT FROM AUTHOR]

In recent years, deep neural networks have enabled countless innovations in the field of image classification. Encouraged by success in this field, researchers worldwide have demonstrated how to use Convolutional Neural Network techniques in medical imaging problems. In this article, the results were obtained through the use of the EfficientNet in the task of classifying 14 different diseases based on chest X-ray images coming from the NIH (National Institutes of Health) ChestX-ray14 dataset. The approach addresses dataset imbalances by introducing a custom split to ensure fair representation. Binary cross entropy loss is utilized to handle the multi-label difficulty. The model architecture comprises an EfficientNet backbone for feature extraction, succeeded by sequential layers including GlobalAveragePooling, Dense, and BatchNormalization. The main contribution of this paper is a proposed solution that outperforms previous state-of-the-art deep learning models average area under the receiver operating characteristic curve—AUC-ROC (score: 84.28%). The usage of the transfer-learning technique and traditional deep learning engineering techniques was shown to enable us to obtain such results on consumer-class GPUs (graphics processing units). [ABSTRACT FROM AUTHOR]