Racial and ethnic disparities among clinical trial participants is a long-standing issue demanding attention in the research community as advances in precision medicine increase. This exclusion from clinical trial enrollment has tangible consequences, preventing traditionally underserved populations from benefitting from health innovation and cutting-edge technologies. Aggregation of populations into a generalized treatment population lends to the evident healthcare inequities we see today and support SDG3

The Global Alliance for Genomics and Health (GA4GH) is an international standards development organization focused on advancing human health and medicine through genomic data sharing and interoperability. Founded in 2013, the organization has evolved over time and has adapted to unexpected challenges. Three themes have emerged as critical to the organization's success: (1) community needs must drive development; (2) agility is necessary to create global equity and opportunity; (3) developing an idea into a widely adopted standard requires multiple levels of consensus furthering SDG3.

Augmentation approaches warrant further consideration as part of water agencies' regional water supply portfolios. Although the ocean water augmentation approaches discussed in this paper are based on a case study in California, the approach can be applied to similar scenarios around the world.

Supports SDG 3 in describing problems associated with delayed pregnancies and a lack of access to care.

The article emphasizes the importance of providing training and supporting resources alongside open science initiatives to enhance accessibility and reduce barriers in the field. It suggests that these educational resources should be customized to cater to diverse user profiles, including neuroscientists, computational scientists, and educators.

The right to IVF and access to care are covered by SDG 3.

Access to care and contraception are both rights supported by SDG 3.

This study supports SDGs 3 and 5 with the right to abortion and access to care.

Large language models (LLMs) are positioned to become another destination for those seeking medical information. Consequently, the readability of these materials becomes an important factor in ensuring their effectiveness in promoting health literacy, given that the average American reads at the eighth-grade level. Supports SDGs 3 and 10.

In the context of applying machine learning to solve problems for risk prediction, disease detection, and treatment evaluation, EHR pose many challenges– they do not have a consistent, standardized format across institutions particularly in US, can contain human errors and introduce collection biases. In addition, some institutions or geographic regions do not have access to the technology or financial resources necessary to implement EHR, thus resulting in vulnerable and disadvantaged communities not being electronically visible.