Enhancing Bias Assessment for Complex Term Groups in Language Embedding Models: Quantitative Comparison of Methods.

Background: Artificial intelligence (AI) is rapidly being adopted to build products and aid in the decision-making process across industries. However, AI systems have been shown to exhibit and even amplify biases, causing a growing concern among people worldwide. Thus, investigating methods of measuring and mitigating bias within these AI-powered tools is necessary.

A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document.

Regulatory agencies consistently deal with extensive document reviews, ranging from product submissions to both internal and external communications. Large Language Models (LLMs) like ChatGPT can be invaluable tools for these tasks, however present several challenges, particularly the proprietary information, combining customized function with specific review needs, and transparency and explainability of the model's output. Hence, a localized and customized solution is imperative.

RxBERT: Enhancing drug labeling text mining and analysis with AI language modeling.

The US drug labeling document contains essential information on drug efficacy and safety, making it a crucial regulatory resource for Food and Drug Administration (FDA) drug reviewers. Due to its extensive volume and the presence of free-text, conventional text mining analysis have encountered challenges in processing these data. Recent advances in artificial intelligence (AI) for natural language processing (NLP) have provided an unprecedented opportunity to identify key information from drug labeling, thereby enhancing safety reviews and support for regulatory decisions.

Measurement and Mitigation of Bias in Artificial Intelligence: A Narrative Literature Review for Regulatory Science.

Artificial intelligence (AI) is increasingly being used in decision making across various industries, including the public health arena. Bias in any decision-making process can significantly skew outcomes, and AI systems have been shown to exhibit biases at times. The potential for AI systems to perpetuate and even amplify biases is a growing concern.

Classifying Free Texts Into Predefined Sections Using AI in Regulatory Documents: A Case Study with Drug Labeling Documents.

The US Food and Drug Administration (FDA) regulatory process often involves several reviewers who focus on sets of information related to their respective areas of review. Accordingly, manufacturers that provide submission packages to regulatory agencies are instructed to organize the contents using a structure that enables the information to be easily allocated, retrieved, and reviewed. However, this practice is not always followed correctly; as such, some documents are not well structured, with similar information spreading across different sections, hindering the efficient access and review of all of the relevant data as a whole.

Simplifying the Measurement of College Students' Career Planning: the Development of Career Student Planning Scale during the COVID-19 Pandemic.

We created a new, 8-item scale called "Career Student Planning Scale (CSPS)" for a valid and reliable measure regarding college students' career planning during a traumatic event, such as a pandemic. CSPS is conceptually similar to the career decision-making difficulty questionnaire (CDDQ) and the career decision self-efficacy (CDSE) scale. CSPS leans towards questions about college students' perceptions about career planning, rather than intuitions about career decision-making; it also inquires about how participants conceptualize about their career plans to be correct, rather than the more extreme idea about how their intuitions are correct: we developed this scale to capture the latter construct.