Artificial intelligence (AI) and machine learning (ML) facilitate the creation of revolutionary medical techniques. Unfortunately, biases in current AI and ML approaches are perpetuating minority health inequity. One of the strategies to solve this problem is training a diverse workforce. For this reason, we created the course “Artificial Intelligence and Machine Learning applied to Health Disparities Research (AIML + HDR)” which applied general Data Science (DS) approaches to health disparities research with an emphasis on Hispanic populations. Some technical topics covered included the Jupyter Notebook Framework, coding with R and Python to manipulate data, and ML libraries to create predictive models. Some health disparities topics covered included Electronic Health Records, Social Determinants of Health, and Bias in Data. As a result, the course was taught to 34 selected Hispanic participants and evaluated by a survey on a Likert scale (0–4). The surveys showed high satisfaction (more than 80% of participants agreed) regarding the course organization, activities, and covered topics. The students strongly agreed that the activities were relevant to the course and promoted their learning (3.71 ± 0.21). The students strongly agreed that the course was helpful for their professional development (3.76 ± 0.18). The open question was quantitatively analyzed and showed that seventy-five percent of the comments received from the participants confirmed their great satisfaction.

Puerto Rico has faced significant damage from natural disasters and the COVID-19 pandemic, disrupting clinical services and scientific research logistics. In response, the RCMI-CCRHD Program at the UPR-MSC organized a retreat with the objective of understanding the challenges faced by its research network during the pandemic and strategies to overcome them. The retreat featured presentations on COVID-19 supplemental projects and included a discussion group. Twenty attendees identified the challenges they encountered and the strategies developed through an open-ended question and a satisfaction survey, implementing a mixed-method approach. We performed a content analysis on the open-ended survey questions and used descriptive statistics for the satisfaction survey. Key challenges included remote work implementation, recruitment difficulties, and mental health concerns. Attendees shared actions taken to address these issues, such as modifying protocols for remote planning and using digital platforms for improving communication. They also recognized opportunities that arose from remote work, which allowed them to focus on publishing and adapting mental health support tools. The retreat received positive feedback, with 93.8% of attendees giving a five-star rating. By reflecting on these experiences, investigators can refine strategies and implement effective solutions. Recommendations include flexible IRB protocols, improved digital tools, community involvement, and robust emergency preparedness plans.