Presented By: Stephanie Richardson, Baylor College of Medicine
Co-Authors: Mostafa Belghasem, Baylor College of Medicine
Steve Charles, Baylor College of Medicine

Purpose
Medical students are expected to have a good ability to interpret histopathological images. This goal is found to be very difficult by many students and can be a time-consuming task. It is even more challenging when a new image is encountered and the students are unable to transfer what they learned in a prior visual setting to the new one, despite knowing what to look for. Therefore, new strategies for improving pathology learning efficiency are needed. The goal of this work is to enhance student's descriptive histopathology skills; and enhance their ability to interpret pathology images and formulate differential diagnoses. 

Methods
Using image editing and processing software, multiple plain layers are added to the high-quality pathology image (layer-0). Each additional plain layer is converted to a smart object and segmentally made transparent to allow certain underlaying key morphological features to be apparent with less distraction from the rest of pathology section. This approach still allows the learner to visualize the section with the key pathology being prominent through the transparent segment of the new layer. These layers can be switched on-and-off to train the learner to recognize the different features and patterns of the pathological lesion through faded-style transitions. Ultimately, the pathology image (layer-0) is converted into an annotated animation and illustration to simplify the pathological changes in a semi-fictional manner. All these layers are eventually exported to PowerPoint compatible format that allows for further adjustments. 

Results 
This approach was evaluated using in-session surveys and post-session surveys. In addition, mandatory formative assessment were used. Student evaluations indicated a very favorable response to this approach of pathology teaching and has significantly increased their confidence in interpreting histopathology images. The vast majority of students (98%) expressed a desire to have these exercises applied for other organ systems and courses. 

Presented By: Anne Farmakidis, AAMC
Co-Authors: Toni Gallo, AAMC
Ethan Kendrick, AAMC
Alexis Rossi, AAMC

Purpose
The AAMC's work in the AI space has been largely membership driven. This presentation provides an overview of the activities, results and next steps for how the organization is supporting the community in implementing and leveraging AI.

Methods
The AAMC has approached AI support in phases based on the needs of the community, with all activities free and open to all AAMC members. The first phase, which was intended as a needs assessment, included a series of community calls, engagement within the virtual community and an informational, town-hall style webinar where constituents learned from each other and experts/early adopters. The AAMC then shifted to a more active role by curating exemplars and best practices in an open access AI resource bundle, developing an AI focused webinar series covering various topics of interest and convening an international AI Committee for Health Professions Education (HPE).

Results
From the needs assessment activities in the first phase, the AAMC learned that that there were several areas of interest that required national support and focus: policies and governance models, examples of using AI for assessment, how to integrate AI in teaching and learning, faculty and staff development, and the continued need to convene and facilitate learning opportunities and conversations across the community.

Conclusions
The AAMC used the feedback collected to guide its future work in supporting AI in HPE. Finding ways to support the entire community regardless of resource and environments remains a critical component of ethical utilization of AI.

Presented By: Marieke Kruidering, New York University Grossman Long Island School of Medicine
Co-Authors: Jeffrey Bird, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell
Judith Brenner, New York University Grossman Long Island School of Medicine
Kumiko Endo, Med2Lab
Tracy Fulton, University of California at San Francisco School of Medicine
Doreen Olvet, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell
Bao Truong, Med2Lab
Joanne Willey, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell

Purpose
The objective is to establish the accuracy of generative artificial intelligence (AI) when scoring medical student exam questions in an open-ended format (OEQ) compared to faculty content experts. Background: Despite the numerous benefits to including OEQs in assessment of medical knowledge1,2, only 39% of US allopathic medical schools use them3. Faculty report that the biggest barrier is the time it takes to grade responses1,2. Natural language processing has been explored to automate scoring of clinical reasoning4, but no study has evaluated the use of generative AI to score OEQ responses in the pre-clerkship curriculum.

Methods
OEQ responses from two questions administered at the Zucker School of Medicine (ZSOM) and the University of California at San Francisco School of Medicine (UCSF) were used for the current study5. Responses from 54 students per site were analyzed. Content experts scored the responses using an analytic (ZSOM) or holistic rubric (UCSF). Questions, rubrics, and student responses were fed into the GPT-4 model via the Med2Lab platform. Once finalized, scores for each student's response were generated. Cohen's weighted kappa (kw) was used to evaluate inter-rater reliability (IRR) between the content expert and generative AI scores, with kw scores between 0.60 and 0.80 being considered substantial6. Prompt engineering was employed for question 1 (analytic rubric) to evaluate its impact on IRR.

Results
IRR between the content expert and generative AI scores was substantial using the analytic rubric (question 1: kw=0.71; question 2: kw=0.63) and the holistic rubric (question 1: kw=0.66; question 2: kw=0.68). IRR for question 1 (analytic rubric) was initially kw=0.61 but was increased to kw=0.71 after adjustments with prompt engineering and re-run in GPT-4.

Conclusions
Generative AI can score OEQs with substantial reliability. With the potential to alleviate grading burden, AI scoring will allow medical schools to broadly implement OEQs for assessment.

Presented By: Kearney Gunsalus, Augusta University/University of Georgia Medical Partnership
Co-Authors: DeLoris Hesse, Augusta University/University of Georgia Medical Partnership

Purpose
To enhance accessibility by developing a color-coding palette for curricular materials that is accessible to people with the three most common types of color vision deficiency (CVD, or color blindness). CVD affects ~8% of men and 0.5% of women, and impacts perception of a wide range of colors. Color-coding is commonly used to convey information in undergraduate medical education; selection of CVD-accessible color schemes is therefore an important accessibility consideration, yet this topic is sparsely represented in the medical education literature. 

Methods
We are using freely available, validated tools to address this issue. CVD simulators allow people with normal color vision to assess how an image or set of colors would appear to people with different forms of CVD, and colorblind-friendly color palettes have been designed for various uses. The color coding in many of our curricular materials is currently not colorblind-friendly. We chose to start by redesigning the color scheme for our pre-clerkship curriculum calendar because it poses several challenges, including the large number of distinct colors required (14) and the need for contrast between font and background colors.

Results
Using CVD simulators, we identified the pink/lilac/light blue, yellow/green, and red/green in our current color scheme as difficult to distinguish for people with protanopia and deuteranopia (deficiencies in perception of red and green light, respectively). We redesigned the color scheme to ensure accessibility for people with the three most common types of CVD and are offering the CVD palette as a theme for use in curricular and pedagogical materials.

Conclusions
The redesigned color scheme will enhance accessibility for students, faculty, and staff. We will provide faculty development on designing CVD-accessible materials. Further work will evaluate impact on faculty awareness of colorblindness and implementation of tools for CVD-accessible design. This innovation could readily be adopted at other institutions with minimal time and effort.

Presented By: Vijay Rajput, Nova Southeastern University Dr. Kiran C. Patel College of Osteopathic Medicine
Co-Authors: Vanessa D'Amario, Nova Southeastern University Dr. Kiran C. Patel College of Osteopathic Medicine
Jorge Cervantes, Nova Southeastern University Dr. Kiran C. Patel College of Osteopathic Medicine
Mohammadali Mohajel Shoja, Nova Southeastern University Dr. Kiran C. Patel College of Osteopathic Medicine
Tanya Ramadoss, Nova Southeastern University Dr. Kiran C. Patel College of Osteopathic Medicine
Blake Smith, Nova Southeastern University Dr. Kiran C. Patel College of Osteopathic Medicine

Purpose
Generative AI (GenAI) is an innovative technology with major impact on faculty and learners in medical education. This work aims to measure the perception of GenAI among medical educators, to gain insights on major advantages and concerns.

Methods
A survey has been distributed across the colleges of allopathic and osteopathic medicine within single university during fall of 2023. The survey was developed by authors that comprises twelve items among those the role of GenAI for students and educators, the need to rethink teaching approaches, perceived advantages, applications of GenAI in the educational context, concerns, challenges, and trustworthiness.

Results
Preliminary results were collected from 48 faculty. They show a positive attitude towards GenAI and disagreement on GenAI having very negative effect on either the students educational experience or the faculty. 41.6% respondents never used GenAI, while the majority relied on GenAI for text generation. The majority agrees that GenAI is likely to change their role as educator. The overwhelming majority of the faculty agrees on the lack of guidelines for safe use of GenAI, from both a governmental and an institutional standpoint. The ability to conduct more efficient research, task automation, and increased contents accessibility are perceived as greatest strengths of GenAI. Among the main perceived challenges are cheating, the GenAI tendency of making errors, and privacy concerns. While consulting GenAI is not necessarily cheating, responders agree that copy-and-paste from GenAI constitutes cheating. While the results show strong polarity, the limitation of this study is that less than half of the group under study filled the survey.

Conclusion
Although GenAI is a newly available technology, most respondents acknowledge the impact of GenAI in education. There is general agreement that plagiarism and lack of regulations are two major areas of concerns. We expect to collect more data in the near future.

Presented By: Joseph Williams, Kansas City University
Co-Authors: Mari Hopper, Kansas City University

Purpose 
Aim of the presentation is to share one osteopathic college's approach to integrating AI training and competency assessment into undergraduate medical curriculum. This project aims to create an accessible, applicable framework that aligns with technological advancements and student-doctors' needs including: understanding AI foundations, aiding clinical decision-making, and preparation for a rapidly changing future in healthcare. 

Methods
Project was launched with a literature review to understand AI applications in healthcare and education best practices. A survey of medical educators and students was conducted to define AI learning objectives and competencies. A flexible, scalable AI curriculum is under development, including integration into new courses, utilizing diverse modalities, including case studies and simulations, and data science, coding, and additional interactive tools. The curriculum's effectiveness is tested through pilot modules, with feedback from students and educators driving continuous improvements. Collaborations with technology and healthcare experts ensure the curriculum remains current with AI advancements and real-world applications. Evaluations using mixed methods to measure the curriculum's impact on student's knowledge, skills, and attitudes provide reflective opportunities and informative updates to the discussions on AI in medical education. 

Results 
Institutional leadership, faculty, and students see the current technological landscape and the need to understand the foundational functions of AI and associated tools. Training pre-clerkship, before the intersection of patients, providers, and emergent technology, allows student doctors a necessary level of adaptability and familiarity to embrace the inevitable changes AI is and will produce in healthcare. 

Conclusion 
AI, machine learning, and prescriptive analytics are evolving rapidly and establishing immense opportunities in healthcare. As health professions educators, we share an ethical obligation to train future providers to be responsible, capable users of this technology. Implementing an AI-focused curriculum in an osteopathic medicine college has presented challenges and opportunities that other health education programs will benefit from.

Presented By: Jyotsna Needamangalam Balaji, Panimalar Medical College Hospital & Research Institute
Co-Authors: Krishna Mohan Surapaneni, Panimalar Medical College Hospital & Research Institute

Purpose
The integration of artificial intelligence (AI) in medical education has been a subject of increasing interest and exploration. One particular aspect gaining attention is the establishment of real-time feedback systems using AI to enhance the learning experience for medical students. This study aims to gather educators' opinions on the implementation of such systems and provide a qualitative analysis of their perspectives.

Methods
In this qualitative study, medical educators were selected through purposive sampling, ensuring representation from diverse backgrounds and institutions. Semi-structured interviews and focus group discussions were conducted to capture a rich and nuanced understanding of educators' opinions on the potential benefits, challenges, and implications of integrating real-time feedback systems powered by AI. The interview and discussion sessions were audio-recorded and transcribed for analysis. Thematic and content analysis was performed with the anonymised data.

Results
The analysis revealed several key themes emerging from the educators' opinions. Positive responses were noted in terms of the potential for AI-driven real-time feedback to offer personalized learning experiences, timely identification of areas for improvement, and the promotion of self-directed learning among students. Educators also expressed optimism about the scalability of such systems and their ability to accommodate the diverse learning styles and paces of individual students. However, challenges were identified, including concerns related to the ethical use of AI, potential bias in algorithms, and the need for comprehensive training for both educators and students to effectively utilize these systems and maintain a balance between technology-assisted learning and human interaction in medical education.

Conclusions
Educators' opinions on establishing real-time feedback systems using AI in medical education reflect a mix of enthusiasm for the potential benefits and cautious consideration of associated challenges. The findings underscore the need for thoughtful and ethical integration of AI technologies, ensuring that they complement rather than replace traditional teaching methods.

Presented By: Erica Sutton, Morehouse School of Medicine
Co-Authors: Courtney Cross, OnlineMedEd

Purpose
While the use of third-party resources has become more prevalent, research on the effects of these resources is minimal. Evaluating their impact is challenging. To gain a deeper understanding of how third-party resources are being integrated and their impacts, it is crucial to continuously evaluate their usage and adoption, and to implement designed interventions if needed. 

Methods 
Morehouse School of Medicine partnered with an online medical education learning platform to provide comprehensive supplemental learning material to 3rd year MD students. The resource was evaluated using the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) program evaluation framework. In this study, we identified measurements for each dimension to comprehensively evaluate the implementation of the learning platform using quantitative data collected by the school and from the platform and qualitative data collected from two learner focus groups. 

Results 
Of the 85 students enrolled, 70 (81%) logged in and completed content. Students with a delayed rotation schedule or participating in the Fee Assistance Program were more likely to use the resource. 100% of students completed video content, while 60% completed reading content. A significantly smaller percentage of students utilized board-style questions or flashcards (p<0.05). Qualitative data showed preferred learning styles and lack of awareness of other learning modalities primarily underlie adoption. Students used the content primarily to prepare for shelf exams. They identified the need to engage with the content to truly learn, usually through notetaking and review. High resource usage was associated with higher Step 2 scores, while low usage was associated with delaying taking the Step 2 exam (p<0.05). 

Conclusion 
Preliminary data using the RE-AIM framework and qualitative feedback indicate positive utilization and outcomes from implementing an online medical education learning platform. Next steps include integration of the platform into the curriculum and implementing strategies to improve Reach, Adoption, and Implementation.

Presented By: Stephanie Moore-Lotridge, Vanderbilt University Medical Center
Co-Authors: Ole Molvig, Vanderbilt University Medical Center
Jewsin Raj, No Affiliation
Chris Schoenecker, No Affiliation
Jonathan Schoenecker, Vanderbilt University Medical Center
Bryan Tompkins, No Affiliation

Purpose
Didactic lectures continue to be an essential complement to hands-on clinical experiences in a post-graduate medical education curriculum. Cognitive psychology experiments suggest that introduction of information, followed by repeated "retrieval" through active recall, is the best way to generate long-term retention. The objective of this study was to build an app-based platform for use in conjunction with resident didactic lectures to 1) promote active recall and 2) provide individualized assessment of the material in real-time.

Methods
An interactive, retrieval-based smartphone application was developed to supplement didactic lectures being presented to orthopaedic residents. The application employed gamified quizzes and survey-based functions that prompted participation from learners, allowing both the presenter and residents to receive instantaneous feedback.

Results
Residents (N=19) reported that standard didactic lectures had variably effectiveness, while lectures given in conjunction with the interactive application were rated as more effective than standard lectures (89.5%-much more effective; 10.5%-somewhat more effective). Importantly, all residents found the application easy to use (84.2%-strongly agreed; 15.8%-somewhat agreed) and noted that they would like to incorporate such a tool when they give future lectures (52.6%-very likely to use; 47.4%-somewhat likely to use). Presenting attending physicians (N=3) commented that they found the application "helpful for guiding discussion" and "identification of areas of deficiency in the audience" aligning with the real-time assessment capacity. 

Conclusions
Incorporating technology in residence lectures was both feasible and impactful to the learner's experiences. Importantly, such a tool has the capacity to promote active recall and provide individualized assessment of the materials in real-time. Based on these positive results, our team will expand the use of this smartphone application to additional residency lectures with the hope of providing longitudinal assessments.

Presented By: Martha Garcia, San Juan Bautista School of Medicine, Universidad Central del Caribe

Purpose
The passion-driven project addresses key issues in health professions education, aiming for a holistic approach, overcoming challenges in implementing active learning strategies, fostering engagement and motivation, and integrating interdisciplinary skills and wellness principles.

Methods
Implemented as a groundbreaking capstone project for health professions students, including both medical and chiropractic students in Puerto Rico, the intervention utilizes design thinking principles. Students work in groups, choosing topics they are passionate about, integrating threshold biomedical science concepts with wellness and health promotion using the 6 pillars of lifestyle medicine as framework. The capstone project has been replicated across seven cohorts of medical students and three cohorts of chiropractic students, totaling 56 and 16 proposals, respectively.

Results
Evaluation results, assessed through a 360-degree approach, indicate the achievement of objectives and alignment with program competencies. Results across participant groups consistently show high student satisfaction (>98%) and faculty observations reveal the development of lifelong learning skills, collaborative teamwork, and advancements in active learning and higher-order thinking abilities. Notably, several student-developed projects are being considered for inclusion in university wellness programs, emphasizing real-world impact.

Conclusions
The intervention's success in achieving its objectives, promoting holistic student development, and contributing to the broader community through wellness initiatives is evident. The lessons learned from this project have significant implications for health professions education, emphasizing the effectiveness of integrating passion-driven learning, design thinking, threshold concepts, and wellness principles. The positive outcomes, coupled with high student satisfaction and the recognition of projects for inclusion in wellness programs, suggest broad applicability across diverse health professions and educational contexts. The emphasis on lifelong learning, teamwork, and higher-order thinking aligns with evolving healthcare professional needs. These positive outcomes highlight the importance of adapting educational strategies to meet the dynamic challenges of health professions education in the future.

Presented By: Shourya Kumar, Texas A&M University School of Engineering Medicine
Co-Authors: Ericka Greene, Houston Methodist Hospital
Paras Gupta, Texas A&M University School of Engineering Medicine
Nick Sears, Texas A&M University School of Engineering Medicine
Tristen Slamowitz, Texas A&M University School of Engineering Medicine

Purpose 
Healthcare innovation demands collaboration between physicians and engineers. Physicians identify clinical needs, but lack technical skills for innovation, while engineers may lack clinical insights. Our solution is the "Biodesign Sprint," an immersive educational initiative fostering collaboration among physicians, engineers, and medical students. Piloted at Texas A&M School of Engineering Medicine (EnMed) and Houston Methodist Hospital (HMH) Neurology and Neurosurgery Departments, the Sprint aims to develop innovative solutions to meet unmet clinical needs. 

Methods 
EnMed students initiated the process, presenting Grand Rounds to HMH Neurosurgery and Neurology faculty, requesting clinical needs submissions. A steering committee comprising clinical experts, biodesign specialists, and engineers evaluated proposals based on impact, scope, and feasibility. Top projects led to member recruitment, building diverse-skill teams. Project facilitators guided members in exploring background information, including disease pathology, IP landscape, and existing solutions. The Sprint involved an interactive session, crafting a "pitch deck," rapid brainstorming, and formulating an implementation plan. Post-Sprint, teams met independently, with facilitators tracking progress through periodic check-ins. 

Results 
Clinicians submitted 17 projects for committee review, from which 4 unique projects spanning disciplines including biomaterials, surgical imaging/navigation, and clinical software development were chosen. Team members were recruited from 7 different departments/institutions that typically operate independently. 52 individuals attended the biodesign sprint. All 4 were launched at the Sprint, and progress will be tracked at 3, 6, 12 month intervals. 

Conclusion 
The primary output is the launching of projects targeted against well-vetted clinical needs, with the perfect team to tackle it, and after our Biodesign Sprint, a roadmap to implementation. Other tangible outputs as projects progress may include prototyping, testing, invention disclosures, IP filing, commercialization and traditional posters/abstracts/presentations, and publications. Importantly, our unique program bridges the traditionally disparate worlds of medicine and engineering, providing a novel process for rapid project initiation.

Presented By: Kevlian Andrew, St. George's University
Co-Authors: Michael Montalbano, St. George's University

Purpose
Artificial intelligence (AI) systems such as ChatGPT are expected to become an efficient faculty resource for content creation. However, previous studies have indicated the propensity of machine learning algorithms to recapitulate negative stereotypes that run counter to the efforts of diversity and equity initiatives. We report here an investigation to test the likelihood of ChatGPT and Microsoft Bing A.I. Image Generator (MBIG) to perpetuate medical stereotypes. 

Methods 
A series of USMLE vignettes and visual representations were requested from ChatGPT and MBIG on five medical conditions traditionally associated with certain ethnic groups: sickle cell disease, cystic fibrosis, Tay-Sachs, beta-thalassemia, and aldehyde dehydrogenase deficiency. Additional prompt engineering was performed iteratively to determine if the tendencies toward certain generated vignettes and imagery were mutable or fixed. 

Results 
In all five vignette and image types, initial iterations returned race-based stereotypical patient backgrounds. Additionally, in all five conditions, questioning of prevalences or requests to reassess patient demographics resulted in initial changing of ethnicity followed by reversion to original default presentations. 

Conclusion 
Prompt engineering and additional specifications can minimize stereotypical medical content generated by AI models such as ChatGPT and MBIG. However, their ability to quickly revert shows that diligence must be maintained if diversity and equity efforts are to progress.

Presented By: Daniel Levine, Kirk Kerkorian School of Medicine at University of Nevada, Las Vegas
Co-Authors: Charissa Alo, Kirk Kerkorian School of Medicine at University of Nevada, Las Vegas
Emily Ames, Kirk Kerkorian School of Medicine at University of Nevada, Las Vegas
Justin Atkins, Kirk Kerkorian School of Medicine at University of Nevada, Las Vegas
Rosalie Kalili, Kirk Kerkorian School of Medicine at University of Nevada, Las Vegas
Colin Standifird, Kirk Kerkorian School of Medicine at University of Nevada, Las Vegas
Thomas Vida, Kirk Kerkorian School of Medicine at University of Nevada, Las Vegas

Purpose
Generative artificial intelligence (AI) systems, built and trained with natural language processing models such as ChatGPT, can potentially make major impacts in medical education. This study determines whether ChatGPT can generate multiple-choice questions (MCQs) suitable for medical education. MCQs have played a pivotal role in medical education for enhancing long-term retention of fundamental concepts. They are the cornerstone of improving learning outcomes through the application of retrieval practice, as well as serving as a key tool for assessing the knowledge of medical students. 

Methods 
We prompted ChatGPT (GPT 3.5) to create 100 MCQs with 5 answer options using information from trusted and reputable medical information resources. To determine validity, we independently prompted the questions back into ChatGPT in two manners. First, we posed the AI-generated questions as open-ended, asking for a free response answer from ChatGPT. Second, we prompted the questions including the answer choices, and scored for correct answers. 

Results 
As free-response questions, ChatGPT answered 84% correctly with approximately 2% of responses not providing sufficient information for a focused answer. However, when the same questions were prompted with multiple-choice options, ChatGPT answered them 95% correctly. 

Conclusions
This work is innovative as it leverages the power of freely available generative AI platforms to construct MCQs. These can be matched to a variety of difficulty levels, affording a customized study plan for medical students. A limitation arises in that the questions may need knowledge from a content expert to verify their accuracy. While more research is needed to determine AI's role in medical education, the use of ChatGPT as a readily available tool in medical school can potentially bridge knowledge gaps. Generative AI is rapidly increasing in accuracy and breadth, making it quite feasible as a low-stakes assessment tool for medical students.

Presented By: Savannah Schauer, Kirk Kerkorian School of Medicine at University of Nevada, Las Vegas
Co-Authors: Justin Atkins, Kirk Kerkorian School of Medicine at University of Nevada, Las Vegas
Daniel Levine, Kirk Kerkorian School of Medicine at University of Nevada, Las Vegas
Kaitlyn Novotny, Kirk Kerkorian School of Medicine at University of Nevada, Las Vegas
Axel Rivas, Kirk Kerkorian School of Medicine at University of Nevada, Las Vegas
Edward Simanton, Kirk Kerkorian School of Medicine at University of Nevada, Las Vegas
Stephanie Wang, Kirk Kerkorian School of Medicine at University of Nevada, Las Vegas

Purpose
Medical education requires students to draw on several resources of varying media forms for knowledge acquisition. One popular written resource, First Aid for the USMLE STEP 1 2023, outlines high-yield information to focus on for exams. Artificial intelligence (AI) programs have also become valuable resources and assist students in innumerable ways. Given the diversity of students' resources and media preferences, this project sought to utilize AI programs to convert the written information of First Aid into an audio file to accommodate auditory learners, an outcome that could have significant implications for the future of medical education.

Methods
We recorded multiple participants' voices on Descript, an AI program used to read written text in the voice of a given participant. Each chapter of First Aid was translated into Google Docs and then ChatGPT was used to edit, simplify explanations, and develop a script. The script was uploaded to Descript and read aloud in the selected person's voice. Final editing on Descript was performed to fix pronunciation errors and overall flow, and eventually the files were exported as MP3s.

Results
Utilizing AI for script creation/editing was relatively simple, and both Google Docs and Descript allowed for virtual collaboration. However, as medical students with minimal technological backgrounds, there were significant learning curves, making audio editing with Descript time consuming. Upon completion, the information from 16 chapters of First Aid for the USMLE STEP 1 2023 were successfully converted into scripts and ultimately audio files.

Conclusion
As advancements in AI progress, their capacity to convert various media formats at minimal cost has the ability to accommodate individualized student learning requirements. This transformative capability has the potential to revolutionize education by expanding the accessibility of medical learning materials, thereby reshaping educational practices.

Presented By: Nancy Moreno, Baylor College of Medicine
Co-Authors: Tyson Pillow, Baylor College of Medicine

Purpose
Educator roles are changing to support learner-centered teaching, technology integration, and inclusive learning environments. In response, institutions are reimagining ways to support educator career pathways, share best practices, foster collaborations, and support educational research and scholarship. At the authors' home institution, a new department focused on education, innovation, and technology was launched--with a broadly inclusive faculty consisting of clinician educators, science educators, educational technologists and designers, and educational researchers. 

Methods 
Initiated in 2021, the Department of Education, Innovation and Technology (EIT) offers an academic home for educators and is building an environment conducive to collaboration and novel approaches. Inaugural faculty were recruited from across the health sciences university and selected by peer review following a holistic review process. The holistic review process included development of a rubric of competencies, attributes and experiences, and scholarly and educational contributions (metrics). 

Results 
Twenty-five faculty members were selected and appointed as an inaugural cohort. This group worked as a team to design and subsequently deliver a novel educator certificate course on an ongoing basis. Community building activities include: peer mentoring circles; faculty writing group; new infrastructure for innovative teaching (self-service video studio and interactive teaching lab); launch of Center for Teaching and eLearning; funding of 10 grants for collaborative faculty research; and an institution-wide education hackathon. In its first year, EIT faculty published more than 65 education-related papers. EIT now has more than 75 faculty members (including 38 primary appointees). Initial focus areas of faculty activities include: science of teaching and learning, curriculum design and assessment, technology applications, and educational research. 

Conclusion 
Creating an academic department focused on teaching and learning offers a new environment for collaboration and recognition of educator contributions. Recruiting educators from diverse disciplines broadens perspectives and contributes to knowledge sharing. The building process requires attending to development of programs, people and structures.

Presented By: Serine Torosian, St. George's University, School of Medicine
Co-Authors: Vanad Mousakhani, Frank H. Netter MD, School of Medicine
Vineeta Ramnuth, St. George's University, School of Medicine
Gabrielle Walcott-Bedeau, St. George's University, School of Medicine
Samantha Wehsener, St. George's University, School of Medicine

Background
The use of virtual reality (VR) training in areas with high-stake outcomes, such as the military, aviation, and medicine, prepares individuals for perilous scenarios within a safe and controlled setting. This review article aims to investigate the application and effectiveness of VR technology during preclinical medical education. 

Method 
A systematic review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta Analyses) guidelines was conducted in July 2023 using the PubMed and Scopus databases and search terms "medical education", "preclinical" and "VR or virtual reality". All relevant studies were screened and collated by two independent reviewers. 

Result 
The search resulted in 17/79 (21.5%) articles meeting the criteria for inclusion. There were articles on medical (n=12), and other healthcare (n=5) preclinical education. Among experimental studies, a statistically significant improvement in student performance and self-efficacy was found in 63.6% (7/11) of the studies. Most of the review articles reported positive student feedback (75.0%, 3/4). Additionally, positive remarks were made about student operation skills and confidence among 80.0% (4/5) of other health education studies. 

Conclusion 
Virtual reality technology promises an improved and immersive experience for learners. Since its first introduction, there has been a growing interest and a positive change in attitude towards the use of VR during education. With continued improvements in technology, it is important to explore the potential for enhancing medical training during the early preclinical years. VR allows students to study anatomical structures that are difficult to visualize on traditional cadavers. It provides animation and visual guides for the easy learning of abstract topics. Additionally, VR simulated learning provides a safe environment, e.g., during OSCE, allowing students opportunities to practice their clinical reasoning and skills. While VR may not fully replace traditional lectures, it has the potential to surpass the usefulness of textbooks for our future medical learners.