Results and implications for generative AI in a large introductory biomedical and health informatics course.

Generative artificial intelligence (AI) systems have performed well at many biomedical tasks, but few studies have assessed their performance directly compared to students in higher-education courses. We compared student knowledge-assessment scores with prompting of 6 large-language model (LLM) systems as they would be used by typical students in a large online introductory course in biomedical and health informatics that is taken by graduate, continuing education, and medical students. The state-of-the-art LLM systems were prompted to answer multiple-choice questions (MCQs) and final exam questions.

A Quarter-Century of Online Informatics Education: Learners Served and Lessons Learned.

The value and methods of online learning have changed tremendously over the last 25 years. The goal of this paper is to review a quarter-century of experience with online learning by the author in the field of biomedical and health informatics, describing the learners served and the lessons learned. The author details the history of the decision to pursue online education in informatics, describing the approaches taken as educational technology evolved over time.

C(sp)-H selenylation of substituted benzo[4,5]imidazo[2,1-]thiazoles using phenyliodine(iii)bis(trifluoroacetate) as a mediator.

Herein, an expeditious metal-free regioselective C-H selenylation of substituted benzo[4,5]imidazo[2,1-]thiazole derivatives was devised to synthesize structurally orchestrated selenoethers with good to excellent yields. This PIFA [bis(trifluoroacetoxy)iodobenzene]-mediated protocol operates under mild conditions and offers broad functional group tolerance. In-depth mechanistic investigation supports the involvement of radical pathways.

Search still matters: information retrieval in the era of generative AI.

Objective: Information retrieval (IR, also known as search) systems are ubiquitous in modern times. How does the emergence of generative artificial intelligence (AI), based on large language models (LLMs), fit into the IR process?

Process: This perspective explores the use of generative AI in the context of the motivations, considerations, and outcomes of the IR process with a focus on the academic use of such systems.

Conclusions: There are many information needs, from simple to complex, that motivate use of IR.