Artificial intelligence models represented in machine learning algorithms are promising tools for risk assessment used to guide clinical and other health care decisions. Machine learning algorithms, however, may house biases that propagate stereotypes, inequities, and discrimination that contribute to socioeconomic health care disparities. The biases include those related to some sociodemographic characteristics such as race, ethnicity, gender, age, insurance, and socioeconomic status from the use of erroneous electronic health record data. Additionally, there is concern that training data and algorithmic biases in large language models pose potential drawbacks. These biases affect the lives and livelihoods of a significant percentage of the population in the United States and globally. The social and economic consequences of the associated backlash cannot be underestimated. Here, we outline some of the sociodemographic, training data, and algorithmic biases that undermine sound health care risk assessment and medical decision-making that should be addressed in the health care system. We present a perspective and overview of these biases by gender, race, ethnicity, age, historically marginalized communities, algorithmic bias, biased evaluations, implicit bias, selection/sampling bias, socioeconomic status biases, biased data distributions, cultural biases and insurance status bias, conformation bias, information bias and anchoring biases and make recommendations to improve large language model training data, including de-biasing techniques such as counterfactual role-reversed sentences during knowledge distillation, fine-tuning, prefix attachment at training time, the use of toxicity classifiers, retrieval augmented generation and algorithmic modification to mitigate the biases moving forward.

The novel coronavirus disease 2019 (COVID-19) pandemic has changed the medical education platform for students in the United States of America (USA). In that light, medical schools had to rapidly rearrange the dynamics of their educational curricula from the traditional platforms, to incorporate telemedicine. The telemedicine platform is supported in many specialties, allowing students various options to continue their education without interruption during the COVID-19 pandemic, and beyond. Telemedicine platforms are projected to grow exponentially due to the COVID-19 pandemic, allowing a segue for medical schools to modify their curricula by incorporating telemedicine programs. These distant-, e-learning (tele-education) programs align with the recommendations and guidelines for practicing social distancing. In this article, we surveyed fourth-year medical students to better understand their views on multiple aspects of e-learning, and its impact on their medical education during the COVID-19 pandemic. We assessed the medical students’ experiences, satisfaction, insight and knowledge with e-learning, tele-education, telehealth, and their related modalities during COVID-19. We provide an organized overview and analysis of the main factors that influence medical education during the COVID-19 pandemic, while bringing forth the main challenges, limitations, and emerging approaches in the field of telemedicine and its application as it relates to medical education and e-learning across medical specialties. We outline the main themes and ideas that the medical students voiced, as to how their medical education is being impacted by the COVID-19 pandemic and how they will incorporate telemedicine and tele-education in their future career. A cross-sectional, mixed-method survey was developed and distributed via Google Surveys to 181 University at Buffalo, Jacobs School of Medicine and Biomedical Sciences, United States of America, 4th year medical students, in December 2020. Results were compiled and analyzed after a 6-day open period for responses to be submitted. The survey instrument consisted of questions that inquire about the students’ perspectives as it relates to their rapid switch from their traditional method of learning to the on-line version of medical education during the COVID-19 pandemic. A total of 65 students responded to the survey, of which 63 completed the survey. More than half of the students (n = 63, 57%) indicated that both their specialty of interest, and (n = 21, 33%) their sub-internships were impacted by the temporary lockdown, due to the COVID-19 pandemic. Students also indicated that the top three specialties that were affected included surgery, internal medicine and obstetrics and gynecology. When the students were asked if they were satisfied with the use of aquifer for their health care e-learning, only 35% of the students were satisfied. The students expressed that the school’s administration team did a good job in developing the new tele-education curriculum for those in clinical training. In addition, responses indicated that students were open to case-based video learning and readings, when combined with the abbreviated clinical exposure during the make-up “clinical immersions periods” allowed for adequate learning. Overall, the survey responses show that more than half, approximately 54% of the medical students utilized telemedicine platforms during their clerkships that were impacted by COVID-19. The 4th-year medical students did not find tele-education and e-learning to be as effective as traditional medical education that combines in-person didactic classroom instructions and in-person face-to-face in hospital clerkships. Students felt that the telemedicine program that was rapidly set up due to the COVID-19 ‘lockdown’ was fragmented, since it was not a formal integration of a telemedicine E-learning program. Students would have preferred more ‘real’ cases to follow, instead of the ready-made, aquifer type of cases. Telemedicine has significant potential to address many of the challenges facing the medical education environment today. We believe now that people have become comfortable with this method of teaching, that even after the pandemic ends, we will continue to see tele-education used as a platform for medical education.

We evaluated the performance of Semantic Clinical Artificial Intelligence (SCAI, pronounced Sky), a large language model (LLM) knowledge resource through usability testing. This pretest-intervention-posttest mixed-methods user interface (UI) design study investigates usability to determine whether the LLM provides a more comprehensive, efficient, and enhanced user-friendly means of delivering end user information as compared to using primary sources of information from the Internet (Web). Our analysis focused on assessing the LLM’s efficiency and usability in helping users arrive at accurate and reliable outcomes, to ultimately determine its value as an innovative tool for packaging and presenting information. Usability test sessions were conducted using the cognitive walkthrough approach, via Zoom. Participants were asked to respond to scenarios using only the LLM, and then only the web, and vice versa. These sessions were followed by user feedback sessions where participants rated their experiences and responded to open-ended questions related to the overall usability and satisfaction with SCAI.

We developed a doctor-facing chatbot named SCAI. We aimed to evaluate response speed and user satisfaction. Ten questions were used to test the response speed of the SCAI chatbot. The response time was long, but overall the participants were satisfied with SCAI.

Vaccination was pivotal in controlling COVID-19 spread during the pandemic, benefiting individuals and communities. However, vaccine ‘hesitancy’ (VH) remains a global issue and a major barrier for to achieving population immunity. Acceptance of the COVID-19 vaccine is complex and influenced by multiple factors including distrust, which often vary among racial and ethnic groups. This in person cross-sectional study included 585 adults who completed a self-administered paper survey. The survey incorporated the adult VH Scale (aVHS) and questions on vaccine status and COVID-19-related knowledge. Zip codes with low vaccine uptake were selected using New York State Department of Health data from Erie and Niagara Counties in Western NY. Researchers collected demographic information along with participants knowledge, attitudes, beliefs, and sources of information about the COVID-19 vaccine. Data was transcribed and analyzed using reflexive thematic analysis and mapped to the Theoretical Domains Framework (TDF). Participants demonstrated limited knowledge about COVID-19 vaccine; 12.7% recognized it as viral, 8.4% as contagious, and only a few knew about respiratory transmission. Attitudes and beliefs about the vaccine and booster varied widely. Trusted sources were health care professionals, government, family, friends, and faith-based organizations. Concerns about vaccine side-effects, distrust, and general hesitancy were frequently mentioned. Findings demonstrated trends in published quantitative research. Mapping responses to the TDF and a 5C Model revealed several factors driving VH and vaccine distrust. Public health strategies must emphasize trusted, fact-based communication, promote vaccine literacy, and support informed decision-making for COVID-19 and broader public health initiatives.