Background: The world has witnessed increased adoption of large language models (LLMs) in the last year. Although the products developed using LLMs have the potential to solve accessibility and efficiency problems in health care, there is a lack of available guidelines for developing LLMs for health care, especially for medical education. Objective: The aim of this study was to identify and prioritize the enablers for developing successful LLMs for medical education. We further evaluated the relationships among these identified enablers. Methods: A narrative review of the extant literature was first performed to identify the key enablers for LLM development. We additionally gathered the opinions of LLM users to determine the relative importance of these enablers using an analytical hierarchy process (AHP), which is a multicriteria decision-making method. Further, total interpretive structural modeling (TISM) was used to analyze the perspectives of product developers and ascertain the relationships and hierarchy among these enablers. Finally, the cross-impact matrix-based multiplication applied to a classification (MICMAC) approach was used to determine the relative driving and dependence powers of these enablers. A nonprobabilistic purposive sampling approach was used for recruitment of focus groups. Results: The AHP demonstrated that the most important enabler for LLMs was credibility, with a priority weight of 0.37, followed by accountability (0.27642) and fairness (0.10572). In contrast, usability, with a priority weight of 0.04, showed negligible importance. The results of TISM concurred with the findings of the AHP. The only striking difference between expert perspectives and user preference evaluation was that the product developers indicated that cost has the least importance as a potential enabler. The MICMAC analysis suggested that cost has a strong influence on other enablers. The inputs of the focus group were found to be reliable, with a consistency ratio less than 0.1 (0.084). Conclusions: This study is the first to identify, prioritize, and analyze the relationships of enablers of effective LLMs for medical education. Based on the results of this study, we developed a comprehendible prescriptive framework, named CUC-FATE (Cost, Usability, Credibility, Fairness, Accountability, Transparency, and Explainability), for evaluating the enablers of LLMs in medical education. The study findings are useful for health care professionals, health technology experts, medical technology regulators, and policy makers.

Introduction: Convolutional Neural Network (CNN) systems in healthcare are influenced by unbalanced datasets and varying sizes. This article delves into the impact of dataset size, class imbalance, and their interplay on CNN systems, focusing on the size of the training set versus imbalance—a unique perspective compared to the prevailing literature. Furthermore, it addresses scenarios with more than two classification groups, often overlooked but prevalent in practical settings. Methods: Initially, a CNN was developed to classify lung diseases using X-ray images, distinguishing between healthy individuals and COVID-19 patients. Later, the model was expanded to include pneumonia patients. To evaluate performance, numerous experiments were conducted with varied data sizes and imbalance ratios for both binary and ternary classifications, measuring various indices to validate the model’s efficacy. Results: The study revealed that increasing dataset size positively impacts CNN performance, but this improvement saturates beyond a certain size. A novel finding is that the data balance ratio influences performance more significantly than dataset size. The behavior of three-class classification mirrored that of binary classification, underscoring the importance of balanced datasets for accurate classification. Conclusions: This study emphasizes the fact that achieving balanced representation in datasets is crucial for optimal CNN performance in healthcare, challenging the conventional focus on dataset size. Balanced datasets improve classification accuracy, both in two-class and three-class scenarios, highlighting the need for data-balancing techniques to improve model reliability and effectiveness. Motivation: Our study is motivated by a scenario with 100 patient samples, offering two options: a balanced dataset with 200 samples and an unbalanced dataset with 500 samples (400 healthy individuals). We aim to provide insights into the optimal choice based on the interplay between dataset size and imbalance, enriching the discourse for stakeholders interested in achieving optimal model performance. Limitations: Recognizing a single model’s generalizability limitations, we assert that further studies on diverse datasets are needed.

Code obfuscation has become an essential practice in modern software development, designed to make source or machine code challenging for both humans and computers to comprehend. It plays a crucial role in cybersecurity by protecting intellectual property, safeguarding trade secrets, and preventing unauthorized access or reverse engineering. However, the lack of transparency in obfuscated code raises significant ethical concerns, including the potential for harmful or unethical uses such as hidden data collection, malicious features, back doors, and concealed vulnerabilities. These issues highlight the need for a balanced approach that ensures the protection of developers’ intellectual property while addressing ethical responsibilities related to user privacy, transparency, and societal impact. This paper investigates various code obfuscation techniques, their benefits, challenges, and practical applications, underscoring their relevance in contemporary software development. This study examines obfuscation methods and tools, evaluates machine learning models—including Random Forest, Gradient Boosting, and Support Vector Machine—and presents experimental results aimed at classifying obfuscated versus non-obfuscated files. Our findings demonstrate that these models achieve high accuracy in identifying obfuscation methods employed by tools such as Jlaive, Oxyry, PyObfuscate, Pyarmor, and py-obfuscator. This research also addresses emerging ethical concerns and proposes guidelines for a balanced, responsible approach to code obfuscation.

Cryptocurrencies are decentralized digital financial services that do not physically exist in the world of tangible products and goods, and therefore purportedly offer some positive environmental sustainability features. However, since they are based on blockchain technology, which requires a relatively large input of energy, their climatic impact is not benign. Furthermore, they are very volatile and characterized by low levels of transparency and control, thus creating some negative economic and social sustainability effects. Stablecoins, which are a pegged type of cryptocurrency, exhibit much less volatility and have higher levels of management and interoperability. This raises the following question: are stablecoins more sustainable compared to other cryptocurrencies? To explore this, a sustainability assessment was conducted, comparing cryptocurrencies and stablecoins across environmental, social, and economic dimensions while identifying the key characteristics of sustainability. It was found that stablecoins can mitigate the economic and social risks associated with cryptocurrencies and thus increase their overall sustainability. Moreover, since stablecoins are managed and governed to a greater extent, a key consideration in their development is the selection and implementation of more appropriate mechanisms that can reduce energy use and enhance sustainability. Finally, stablecoins offer more effective—and not just more efficient—solutions, based on value co-creation between several providers and a customer.

The absorbance of the evanescent waves of infrared radiation transmitted through an optical fiber depends on the geometry of the fiber in addition to the wavelength of the electromagnetic radiation. The signal can thus be enhanced by flattening the midsection of the fiber. While the dependence of the absorbance on the thickness of the midsection has already been studied and experimented upon, we demonstrate that similar results are obtained using Monte Carlo methods based simply on geometrical optics, given the dimensions of the fiber and the power distribution of the fired rays. The optimization can be extended to fibers with more complex geometries of the sensor.