How the National Library of Medicine should evolve in an era of artificial intelligence.

Objectives: This article describes the challenges faced by the National Library of Medicine with the rise of artificial intelligence (AI) and access to human knowledge through large language models (LLMs).

Background And Significance: The rise of AI as a tool for the acceleration and falsification of science is impacting every aspect of the transformation of data to information, knowledge, and wisdom through the scientific processes.

Approach: This perspective discusses the philosophical foundations, threats, and opportunities of the AI revolution with a proposal for restructuring the mission of the National Library of Medicine (NLM), part of the National Institutes of Health, with a central role as the guardian of the integrity of scientific knowledge in an era of AI-driven science.

Using technology to increase reach and optimize consent experience for a large-scale research program.

The consent process for research studies can be burdensome for potential participants due to complex information and lengthy consent forms. This pragmatic study aimed to improve the consent experience and evaluate its impact on participant decision making, study knowledge, and satisfaction with the In Our DNA SC program, a population-based genomic screening initiative. We compared two consent procedures: standard consent (SC) involving a PDF document and enhanced consent (EC) incorporating a pictograph and true or false questions.

Natural Language Processing and Social Determinants of Health in Mental Health Research: AI-Assisted Scoping Review.

Background: The use of natural language processing (NLP) in mental health research is increasing, with a wide range of applications and datasets being investigated.

Objective: This review aims to summarize the use of NLP in mental health research, with a special focus on the types of text datasets and the use of social determinants of health (SDOH) in NLP projects related to mental health.

Methods: The search was conducted in September 2024 using a broad search strategy in PubMed, Scopus, and CINAHL Complete.

Using large language models for extracting stressful life events to assess their impact on preventive colon cancer screening adherence.

Background: Increase in early onset colorectal cancer makes adherence to screening a significant public health concern, with various social determinants playing a crucial role in its incidence, diagnosis, treatment, and outcomes. Stressful life events, such as divorce, marriage, or sudden loss of job, have a unique position among the social determinants of health.

Methods: We applied a large language model (LLM) to social history sections of clinical notes in the health records database of the Medical University of South Carolina to extract recent stressful life events and assess their impact on colorectal cancer screening adherence.

Machine learning in healthcare citizen science: A scoping review.

Objectives: This scoping review aims to clarify the definition and trajectory of citizen-led scientific research (so-called citizen science) within the healthcare domain, examine the degree of integration of machine learning (ML) and the participation levels of citizen scientists in health-related projects.

Materials And Methods: In January and September 2024 we conducted a comprehensive search in PubMed, Scopus, Web of Science, and EBSCOhost platform for peer-reviewed publications that combine citizen science and machine learning (ML) in healthcare. Articles were excluded if citizens were merely passive data providers or if only professional scientists were involved.