In low-resource settings, babies born with gastroschisis — a congenital condition in which the developing intestines extend outside the body through a hole in the abdominal wall —face life-threatening challenges. While survival rates in high-income countries now exceed 90% thanks to advanced medical tools and neonatal care, infants in resource-constrained medical settings still face high mortality rates, partially because of a lack of access to the lifesaving equipment needed to treat the condition. A team of engineers and pediatric surgeons led by Rice University’s Rice360 Institute for Global Health Technologies is working to change that. Their innovation? A simple, low-cost and locally manufacturable medical device, known as the “SimpleSilo,” designed to provide lifesaving treatment for gastroschisis at a fraction of the current cost and made from locally available materials.

Researchers tested a large language model (LLM) on peer review tasks for cancer research papers. They found the AI could be abused to generate highly persuasive rejection letters and other fraudulent reviews, such as requests to cite unrelated papers. Crucially, current AI detection tools were largely unable to identify the AI-generated text, posing a significant, hidden threat to academic integrity.

The objective of this study is to assess the diagnostic performance of image analysis-capable generative AI (Gen-AI) (GPT-4-turbo, Google DeepMind's Gemini-pro-vision, and Anthropic’s Claude-3-opus) in interpreting CT images of lung cancer. This is the first study to integrate the diagnostic capabilities of these three models across distinct imaging settings. Additionally, a Likert scale is used to evaluate each model's internal tendencies. By examining the potential and limitations of multimodal large language models (MM-LLMs) for lung cancer diagnosis, this research aims to provide an evidence-based foundation for the future clinical applications of Gen-AI.