Can ChatGPT generate practice question explanations for medical students, a new faculty teaching tool?

Introduction: Multiple-choice questions (MCQs) are frequently used for formative assessment in medical school but often lack sufficient answer explanations given time-restraints of faculty. Chat Generated Pre-trained Transformer (ChatGPT) has emerged as a potential student learning aid and faculty teaching tool. This study aims to evaluate ChatGPT's performance in answering and providing explanations for MCQs.

Where are we now? Evaluating the one year impact of an anti-racism curriculum review.

Purpose: Undergraduate medical education has had a call to action to acknowledge racist practices that are impacting learners throughout their training. In 2020, our school performed a detailed curricular review and provided recommendations to address racism in the curriculum. Many schools have now undergone a similar curricular review process, but little is known about whether suggested antiracist curricular changes impact faculty teaching behavior or the overall curriculum.

Curricular Reform in Serious Illness Communication and Palliative Care: Using Medical Students' Voices to Guide Change.

Purpose: To gather and leverage the voices of students to drive creation of required, integrated palliative care curricula within undergraduate medical education in Massachusetts, which is lacking in a majority of U.S. medical schools.

A Call to Action: Using Curriculum Mapping at Four Medical Schools in Massachusetts to Advance Serious Illness Communication Training in Undergraduate Medical Education.

Practicing physicians require serious illness communication (SIC) skills to ensure high-quality, humanistic care for patients and families as they face life-changing medical decisions. However, a majority of U.S.

Identifying Student Mistreatment Through Peer-Facilitated Learning Environment Sessions.

Problem: Data from the Association of American Medical Colleges (AAMC) Medical School Graduation Questionnaire (GQ) show persistent high rates of medical student mistreatment, and multiple barriers to student reporting of mistreatment exist. The authors examined whether learning environment sessions (LESs) allow students opportunities to identify and describe patterns of mistreatment missed by other avenues of reporting.

Approach: Peer-facilitated LESs were instituted in 2018-2019 at Boston University School of Medicine.

Virtual Reality Simulated Learning Environments: A Strategy to Teach Interprofessional Students About Social Determinants of Health.

Purpose: Physician assistants (PAs) and medical degree students (MDs) often lack training in addressing the social determinants of health (SDOH). Social work students (SWs), meanwhile, have extensive SDOH training; however, few medical professionals have opportunities to engage in interprofessional training with SWs. This study examined the feasibility, acceptability, and students' perceptions of an interprofessional virtual reality (VR) simulated learning environment (SLE) for teaching health professions students about the SDOH.

Deconstructing Racism, Hierarchy, and Power in Medical Education: Guiding Principles on Inclusive Curriculum Design.

In the context of current U.S. racial justice movements, analysis of racism in medicine within medical education is a critical task for all institutions.

Training Medical Students as Peer-Facilitators to Identify Medical Student Mistreatment in the Clerkship Year.

Introduction: Data from the Association of American Medical Colleges' Medical School Graduation Questionnaire show persistent trends of medical student mistreatment nationwide. To reduce the barriers and increase actionable reporting of mistreatment, we integrated peer-facilitated learning environment sessions led by a group of trained third- and fourth-year medical students in all core clinical clerkships.

Methods: During the 2018-2019 academic year, third-year medical students were recruited, oriented, and trained to act as facilitators of sessions on mistreatment.

Machine learning and medical education.

Artificial intelligence (AI) driven by machine learning (ML) algorithms is a branch in computer science that is rapidly gaining popularity within the healthcare sector. Recent regulatory approvals of AI-driven companion diagnostics and other products are glimmers of a future in which these tools could play a key role by defining the way medicine will be practiced. Educating the next generation of medical professionals with the right ML techniques will enable them to become part of this emerging data science revolution.