Assessing the Quality and Reliability of AI-Generated Responses to Common Hypertension Queries.

INTRODUCTION: The integration of artificial intelligence (AI) in healthcare, particularly through language models like ChatGPT and ChatSonic, has gained substantial attention. This article explores the utilization of these AI models to address patient queries related to hypertension, emphasizing their potential to enhance health literacy and disease understanding. The study aims to compare the quality and reliability of responses generated by ChatGPT and ChatSonic in addressing common patient queries about hypertension and evaluate these AI models using the Global Quality Scale (GQS) and the Modified DISCERN scale. METHODS: A virtual cross-sectional observational study was conducted over one month, starting in October 2023. Ten common patient queries regarding hypertension were presented to ChatGPT (https://chat.openai.com/) and ChatSonic (https://writesonic.com/chat), and the responses were recorded. Two internal medicine physicians assessed the responses using the GQS and the Modified DISCERN scale. Statistical analysis included Cohen's Kappa values for inter-rater agreement. RESULTS: The study evaluated responses from ChatGPT and ChatSonic for 10 patient queries. Assessors observed variations in the quality and reliability assessments between the two AI models. Cohen's Kappa values indicated minimal agreement between the evaluators for both the GQS and Modified DISCERN scale. CONCLUSIONS: This study highlights the variations in the assessment of responses generated by ChatGPT and ChatSonic for hypertension-related queries. The findings highlight the need for ongoing monitoring and fact-checking of AI-generated responses.

Research Experiences via Integrating Simulations and Experiments (REVISE): A Model Collaborative Research Project for Undergraduate Students in CO2 Sorbent Design.

Undergraduate research experiences are an instrumental component of student development, increasing conceptual understanding, promoting inquiry-based learning, and guiding potential career aspirations. Moving one step further, as research continues to become more interdisciplinary, there exists potential to accelerate student growth by granting additional perspectives through collaborative research. This study demonstrates the utilization of a model collaborative research project, specifically investigating the development of sorbent technologies for efficient CO2 capture, which is an important research area for improving environmental sustainability. A model CO2 sorbent system of heteroatom-doped porous carbon is utilized to enable students to gain knowledge of adsorption processes, through combined experimental and computational investigations and learnings. A particular emphasis is placed on creating interdisciplinary learning experiences, exemplified by using density functional theory (DFT) to understand molecular interactions between doped carbon surfaces and CO2 molecules as well as explain underlying physical mechanisms that govern experimental results. The experimental observations about CO2 sorption performance of doped ordered mesoporous carbons (OMCs) can be correlated with simulation results, which can explain how the presence of heteroatom functional groups impact the ability of porous carbon to selectively adsorb CO2 molecules. Through an inquiry-focused approach, students were observed to couple interdisciplinary results to construct holistic explanations, while developing skills in independent research and scientific communications. This collaborative research project allows students to obtain a deeper understanding of sustainability challenges, cultivate confidence in independent research, prepare for future career paths, and most importantly, be exposed to strategies employing interdisciplinary research approaches to address scientific challenges.