Artificial intelligence (AI) technologies are poised to become an increasingly important part of education in the anatomical sciences. OpenAI has also introduced generative pretrained transformers (GPTs), which are customizable versions of the standard ChatGPT application. There is little research that has explored the potential of GPTs to serve as intelligent tutoring systems for learning the anatomical sciences. The objective of this study was to describe the design and explore the performance of AnatomyGPT, a customized artificial intelligence application intended for anatomical sciences education. The AnatomyGPT application was configured with GPT Builder by uploading open-source textbooks as knowledge sources and by providing pedagogical instructions for how to interact with users. The performance of AnatomyGPT was compared with ChatGPT by evaluating the responses of both applications to prompts of the National Board of Medical Examiners (NBME) sample items with respect to accuracy, rationales, and citations. AnatomyGPT achieved high scores on the NBME sample items for Gross Anatomy, Embryology, Histology, and Neuroscience and scored comparably to ChatGPT. In addition, AnatomyGPT provided several citations in the responses that it generated, while ChatGPT provided none. Both GPTs provided rationales for all sample items. The customized AnatomyGPT application demonstrated preliminary potential as an intelligent tutoring system by generating responses with increased citations as compared with the standard ChatGPT application. The findings of this study suggest that instructors and students may wish to create their own custom GPTs for teaching and learning anatomy. Future research is needed to further develop and characterize the potential of GPTs for anatomy education.
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http://dx.doi.org/10.1002/ca.24178 | DOI Listing |
Purpose: Accurate identification of radiographic landmarks is fundamental to characterizing glenohumeral relationships before and sequentially after shoulder arthroplasty, but manual annotation of these radiographs is laborious. We report on the use of artificial intelligence, specifically computer vision and deep learning models (DLMs), in determining the accuracy of DLM-identified and surgeon identified (SI) landmarks before and after anatomic shoulder arthroplasty.
Materials & Methods: 240 true anteroposterior radiographs were annotated using 11 standard osseous landmarks to train a deep learning model.
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Department of Medicine, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong; ZeBlast Technology Limited, Hong Kong Science Park;
Intravenous (IV) injection is widely recognized as the most effective and commonly utilized method for achieving systemic delivery of substances in mammalian research models. However, its application in adult zebrafish for drug delivery, stem cell transplantation, and regenerative and cancer studies has been limited due to the challenges posed by their small body size and intricate blood vessels. To overcome these limitations, alternative injection techniques such as intracardiac and retro-orbital (RO) injection have been explored in the past for stem cell transplantation in adult zebrafish.
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