Enhancing Security and Strengthening Defenses in Automated Short-Answer Grading Systems

This study examines vulnerabilities in transformer-based automated short-answer grading systems used in medical education, with a focus on how these systems can be manipulated through adversarial gaming strategies. Our research identifies three main types of gaming strategies that exploit the system's weaknesses, potentially leading to false positives. To counteract these vulnerabilities, we implement several adversarial training methods designed to enhance the systems' robustness. Our results indicate that these methods significantly reduce the susceptibility of grading systems to such manipulations, especially when combined with ensemble techniques like majority voting and ridge regression, which further improve the system's defense against sophisticated adversarial inputs. Additionally, employing large language models such as GPT-4 with varied prompting techniques has shown promise in recognizing and scoring gaming strategies effectively. The findings underscore the importance of continuous improvements in AI-driven educational tools to ensure their reliability and fairness in high-stakes settings.

A Comprehensive Review of Generative AI in Healthcare

The advancement of Artificial Intelligence (AI) has catalyzed revolutionary changes across various sectors, notably in healthcare. Among the significant developments in this field are the applications of generative AI models, specifically transformers and diffusion models. These models have played a crucial role in analyzing diverse forms of data, including medical imaging (encompassing image reconstruction, image-to-image translation, image generation, and image classification), protein structure prediction, clinical documentation, diagnostic assistance, radiology interpretation, clinical decision support, medical coding, and billing, as well as drug design and molecular representation. Such applications have enhanced clinical diagnosis, data reconstruction, and drug synthesis. This review paper aims to offer a thorough overview of the generative AI applications in healthcare, focusing on transformers and diffusion models. Additionally, we propose potential directions for future research to tackle the existing limitations and meet the evolving demands of the healthcare sector. Intended to serve as a comprehensive guide for researchers and practitioners interested in the healthcare applications of generative AI, this review provides valuable insights into the current state of the art, challenges faced, and prospective future directions.