United States

Background: There is much excitement and curiosity about artificial intelligence (AI) and machine learning (ML) among medical students and professionals, especially regarding the impact of automation in medicine. Despite the enormous impact that these technologies are poised to have in the delivery of healthcare, few programs exist to deliberately expose future physicians to the role of AI and ML in medicine, especially those without a background in data science or programming. While online courses exist to teach programming skills and machine learning model development, these are inaccessible to most medical students with limited time and varying technical backgrounds. We designed an interactive workshop to introduce students to basic AI/ML concepts and allow them to train a machine learning model without writing any code. The intended audience was students with no technical or coding background.
Methods: The 45-minute workshop consisted of a 25- minute didactic introduction to ML concepts, ethics, and limitations, and a 20-minute interactive application of ML. Using Teachable Machine, an online interface, students trained diagnostic models to classify dermatologic lesions. The workshop was conducted three times at Emory University School of Medicine, once virtually (September 2020) and twice in-person (November 2021 and April 2023). Anonymous pre- and post-workshop surveys were conducted to measure conceptual and technical understanding of AI/ML, awareness of applications and limitations, and interest in contributing to AI/ML innovation. Data were analyzed by the Wilcoxon Rank Sum test.
Results: Forty-eight total students attended the three sessions. The two in-person sessions in November 2021 and April 2023 were quantitatively evaluated; 26 in-person participants (87%) found the workshop helpful and enjoyable, reporting significant increases (p&lt;0.001) for understanding of the difference between ML and AI (84%), ML applications in medicine (50%), and ML limitations (67%). We also observed a 13% increase in the group’s interest in contributing to AI/ML innovation (p=0.009).
Conclusion: This intervention shows that less than an hour of didactic and interactive training effectively lowers barriers to the promising fields of AI/ML, improving medical student competencies and attitudes towards machine learning.


AMA Research Challenge 2023

An Interactive Introduction to Machine Learning for Medical Students

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NAML 2021

The annual workshop on Naval Applications of Machine Learning is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team. The goals of NAML are to spread awareness of current machine learning research relevant to Naval applications, to connect machine learning researchers with experts in military requirements and to build and strengthen collaborations within the defense research community.


Background: There is much excitement and curiosity about artificial intelligence (AI) and machine learning (ML) among medical students and professionals, especially regarding the impact of automation in medicine. Despite the enormous impact that these technologies are poised to have in the delivery of healthcare, few programs exist to deliberately expose future physicians to the role of AI and ML in medicine, especially those without a background in data science or programming. While online courses exist to teach programming skills and machine learning model development, these are inaccessible to most medical students with limited time and varying technical backgrounds. We designed an interactive workshop to introduce students to basic AI/ML concepts and allow them to train a machine learning model without writing any code. The intended audience was students with no technical or coding background.
Methods: The 45-minute workshop consisted of a 25- minute didactic introduction to ML concepts, ethics, and limitations, and a 20-minute interactive application of ML. Using Teachable Machine, an online interface, students trained diagnostic models to classify dermatologic lesions. The workshop was conducted three times at Emory University School of Medicine, once virtually (September 2020) and twice in-person (November 2021 and April 2023). Anonymous pre- and post-workshop surveys were conducted to measure conceptual and technical understanding of AI/ML, awareness of applications and limitations, and interest in contributing to AI/ML innovation. Data were analyzed by the Wilcoxon Rank Sum test.
Results: Forty-eight total students attended the three sessions. The two in-person sessions in November 2021 and April 2023 were quantitatively evaluated; 26 in-person participants (87%) found the workshop helpful and enjoyable, reporting significant increases (p<0.001) for understanding of the difference between ML and AI (84%), ML applications in medicine (50%), and ML limitations (67%). We also observed a 13% increase in the group’s interest in contributing to AI/ML innovation (p=0.009).
Conclusion: This intervention shows that less than an hour of didactic and interactive training effectively lowers barriers to the promising fields of AI/ML, improving medical student competencies and attitudes towards machine learning.

poster

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The importance of understanding the healthcare needs of individuals with skin of color (SoC) in dermatology has gained increasing awareness. Though research suggests dermatologic literature is one of the key sources providers reference for patient care, the focus is primarily on lighter skin tones, which can be detrimental in providing care for patients with SoC. While there has been recent emphasis on incorporating SoC-focused lectures in residency training, we believe there remains a deficit of this knowledge due to the deficiency of instruction throughout medical school curricula. This study analyzes medical students’ comfort with diagnosing dermatologic conditions among different presentations based on their didactic training.
First and second-year Rocky Vista University medical students (n=22) attended an educational workshop where they were presented with seven dermatologic conditions and asked to diagnose each beginning with a SoC patient, followed by an identical condition in a Caucasian patient, and finally a medical vignette with common descriptors of the condition. Students completed an anonymous survey before and after the seminar to evaluate their comfort level change in identifying dermatologic lesions. Participants' self-reported their comfort in diagnosing these conditions on a scale of 1-10. The number of correct diagnoses was also analyzed.
Prior to the workshop, participants had a mean comfort level of 6.04 ± 1.84 for Caucasian presentations and 4.09 ± 1.54 for SoC presentations. After the seminar, the comfort level increased to 7.04 ± 1.79 for Caucasian presentations and 6.18 ± 2.01 for SoC presentations. Nineteen participants provided anonymous diagnoses for each condition in SoC, caucasian, and clinical vignette presentations. The average percentage of correct answers were 23.26% for SoC presentations, 34.58% for caucasian presentations, and 68.42% for clinical vignettes.
Based on our sample, medical students exhibited higher comfort levels in diagnosing dermatologic conditions in Caucasian skin tones compared to SoC presentations, and correct diagnoses also favored Caucasian presentations over SoC patients. However, following the seminar, the greatest increases in comfort level resulted in SoC presentations, indicating the effectiveness of this instruction in didactic training. Clinical vignettes received the highest correct diagnoses, highlighting the reliance on terminology rather than visual interpretation in early medical education. These findings emphasize the need for diversity of visual dermatological presentations in early medical education and the importance of shifting away from excessive reliance on "buzzwords" in clinical vignettes. By addressing curricular gaps in medical training, we hope to enhance the quality of care for patients with SoC.

How Comfortable are Medical Students with Interpreting Dermatologic Conditions During Didactic Training?

**Scoring is only possible in the semifinals hall**

**Abstract:**



Skin Conditions and Villainous Depictions in Anime Films: Exploring Unconscious Bias in Patient-Provider Interactions

Background: Rural communities rely heavily on EMS teams to provide high quality care during transport to often distant medical facilities. Compliance with standardized protocols has been shown to increase triage effectiveness on a global scale, and our providers use the EMS Prehospital Treatment Protocol Handbook to guide care. The purpose of this study is to evaluate if the clinical documentation of our EMS providers follows standard protocols. 

Methods: Tri-County regional trauma calls in the Maine Incident Reporting System database from July 1st, 2022 to July 31st, 2022 were reviewed. The logs were compared to the EMS Prehospital Treatment Protocol Handbook. Documentation was analyzed for inclusion of relevant details as outlined by the appropriate protocol. The author of each log was documented to establish trends among personnel.

Results: 121 cases were included in the final analysis. The most common chief concerns included falls (n = 76) and motor vehicle collisions (n = 24). The most commonly used protocol was the Trauma Triage protocol. 28 cases (23.1%) did not include a Glasgow Coma Scale (GCS). Two of the GCS scores were below 8, but neither patient was intubated. Of the 28 cases without GCS documentation, 20 cases involved the chief concern of “fall” (71.4%) and 7 were motor vehicle collisions (25%). Of the 38 unique authors, 13 providers were responsible for the cases without GCS documentation, with two providers being responsible for 11 of the 28 cases.

Conclusion: The Trauma Triage protocol was broadly used to cover injuries ranging from standing height falls to head on motor vehicle collisions. Although it is possible that clinical care was provided appropriately when a GCS was not recorded, it could also indicate that our patients were not triaged effectively in the field. Given that two providers accounted for 39% of documentation issues, targeted education could drastically increase the rate of GCS documentation in our group. GCS could also be programmed as a required field for all trauma calls in order to increase compliance throughout the entire group. Locating more validated trauma protocols based on the mechanism of injury could further define how patients need to be triaged as opposed to using one protocol for all trauma. The next step in this study would be to investigate how the rates of GCS documentation changed after implementation of these proposed strategies. 

References:
Van Rein, E.A.J. et al. “Compliance to prehospital trauma triage protocols worldwide: A systematic review.” Injury. 2018; 49(8): 1373-1380. https://pubmed.ncbi.nlm.nih.gov/30135040/

Documentation of Prehospital Treatment Protocols in Emergency Medical Services Trauma Calls

Introduction
Clostridium difficile (C. diff) is the leading cause of nosocomial diarrhea and has been shown to impact about 13 in every 1000 hospitalized patients. The occurrence of C. diff and Inflammatory bowel disease (IBD) have been linked to disruption of the gut microbiota (dysbiosis) which may explain the parallel rise of C. diff infection in IBD patients. Our study aimed to analyze the cost burden and clinical outcomes of hospitalized IBD patients with C. diff infections.
Methods
240,550 adults hospitalized with Crohn’s disease or Ulcerative colitis were analyzed using the NIS data from 2016-2019. Patients were classified into a group with C. diff and a group without C. diff and variables like Age, gender, race, diabetes mellitus, nicotine use, anemia, end-stage renal disease (ESRD), thrombocytopenia, albumin abnormality, peritoneal abscess, toxic megacolon, gastrointestinal bleed, vesicovaginal fistula, anorectal fistula, vesicointestinal fistula, mortality, and hospitalization cost were analyzed using the two-sample t test, chi-square test, and multiple linear regression. P value < 0.05 was considered statistically significant.
Results
We found that compared to men, women hospitalized with IBD are more likely to get infected with C. diff (4.2% vs 4.5%, P = < 0.001). Older IBD patients (Mean age of 53.1) are at risk compared to other age groups (P = < 0.001). Whites had the least C. diff rates compared to blacks, Hispanics, Asians, Native America, and others (4.2% vs 4.7% vs 5.6% vs 5.3% vs 4.4% vs 4.5%, P = < 0.001). IBD patients with anemia and thrombocytopenia had a significantly higher rate of C. diff infection (P = < 0.001). Interestingly, nicotine dependence and peritoneal abscess protected against C. diff infection (P = < 0.001 and 0.008). The average total hospital cost among IBD patients with C. diff was $20,254 higher compared to those without C. diff after controlling the effects of the variables - age, gender, race, nicotine status, anemia, ESRD, peritoneal abscess, thrombocytopenia, gastrointestinal bleed, and vesicovaginal fistula. The odds of mortality among IBD patients with C. diff was more than twice compared to those without C. diff (OR = 2.1, p = < 0.001).
Discussion
IBD patients remain at increased risk of C. diff infection and more hospital expenses. Hence, clinicians should rule out C. diff infection first in every IBD patient presenting with disease flare-ups or diarrhea.

In-hospital Mortality, Outcomes and Cost Analysis of Hospitalized Inflammatory Bowel Disease Patients with Clostridium difficile Infection.


Introduction:
High-fidelity three-dimensional (3D) models provide a better understanding of spatial relationships among complex anatomical structures. Medical students often lack hands-on opportunities to develop an in-depth understanding of applied anatomy prior to their clinical years, a trend exacerbated by the COVID-19 pandemic. There is limited research on the utilization of 3D models in the clinical years. This study evaluates the effectiveness of an interactive 3D-printed inguinal hernia model in teaching applied surgical anatomy to third-year medical students (MS3).

Materials and Methods:
3D-printed models of inguinal anatomy were created using de-identified CT scans, 3D-modeling software, and 3D-printing services. A single-blind randomized control trial of third-year medical students was conducted at a single academic institution. The tactile-group received three educational tools describing inguinal hernia anatomy and repair: an established video module (WISE-MD), an illustrated procedure guide, and a 3D-printed model. The non-tactile group was provided the WISE-MD module and the illustrated procedure guide. Both groups received pre- and post-tests on basic and applied anatomy. Statistical analysis was performed using a t-test or Mann–Whitney U test for continuous variables and Chi-square or Fisher's exact tests for categorical variables. Statistical significance was defined by p <0.05.

Results:
One hundred and three third-year medical students participated in this study. There were no differences in pre-test scores between study groups (p= 0.98). The tactile-group scored better on post-test questions on applied anatomy (p= 0.03), but no differences were noted in scores on basic anatomy questions (p= 0.41). When comparing performance between overall pre-test and post-test scores, the tactile group demonstrated improvement in test scores which trended toward significance (p= 0.09).

Conclusions:
Third-year medical students using high-fidelity 3D-printed models demonstrated improved test scores on applied anatomy when compared to students who were exposed to conventional educational resources. With the rising use of virtual learning environments, interactive learning tools like 3D-printed anatomy models may have an important role in knowledge acquisition during the surgical clerkship.

3D Printing in Surgical Education:A Visuospatial and Kinesthetic Learning Module of Inguinal Hernia Repair for Third-Year Medical Students

Background
Type 2 diabetes mellitus (T2DM) is a global epidemic affecting approximately 9.3% of the world population and has an all-cause mortality rate of 8.5%. An increasing number of patients with chronic conditions access online resources for educational purposes, with recent statistics suggesting that up to 80% of adults in the United States have obtained medical information through online search engines. This study aims to assess the accuracy of the information offered by artificial intelligence (AI) chatbots to T2DM questions using ChatGPT.

Methods:
An initial set of questions about T2DM and its complications were obtained from an online platform such as American Diabetes Association. Subsequently, these questions underwent a thorough evaluation by physicians well-versed in diabetes management before being presented to ChatGPT. Each question was introduced to ChatGPT three times, and the responses were recorded. Two study staff reviewed the three sets of responses and summarized the majority response that was repeated at least twice. Data were reviewed by two board-certified internal medicine physicians with a minimum of 11 years of experience. Responses were categorized into appropriate; provide accurate information that meets the standard of care, inappropriate; contain erroneous information but meets the minimal standard of care, and unreliable; the response provides inconsistent content that does not meet the standard of care.


Results:
Of the original 110 questions, 70 were included in the final dataset. Out of the 70 summarized responses generated by ChatGPT, 89.3% were categorized as appropriate, indicating that the AI model provided accurate information meeting the standard of care for Type 2 diabetes management and its associated complications. On the other hand, 10% of the responses were deemed inappropriate, containing errors but still meeting the minimal standard of care in addressing the questions. The remaining 0.7% of ChatGPT's responses were classified as unreliable, displaying inconsistent content that did not meet the standard of care in terms of accuracy and reliability


Conclusion
ChatGPT offers a promising source of information for educating patients with T2DM. The study demonstrates high accuracy in providing appropriate responses that align with the standard of care, positioning it as a useful tool in enhancing patient education and diabetes management.


Artificial Intelligence Shifting the Paradigm of Diabetes Care

Background
Hypertension is the number one preventable risk factor for all-cause mortality, affecting up to 45% of the adult population worldwide. In the era of the growing influence of artificial intelligence (AI), reliance on online platforms for medical information is more than ever before, resulting in concerns surrounding the accuracy of the available information. This study aims to assess the accuracy of text-generating AI using ChatGPT in answering patients’ frequently asked questions about hypertension and its complications.
methods
To assess the accuracy of the information offered by ChatGPT on hypertension and secondary complications, this nonhuman-subject qualitative study was designed. Frequently asked questions were gathered by study staff from professional platforms, including the American Heart Association. These questions were then reviewed by two physicians experienced in treating hypertension, and a final set of questions was established. Each question was put through the ChatGPT three times, and the majority- response to each question was evaluated against the national guidelines by study staff who graduate physicians in internal medicine. A board-certified internal medicine physician with over 8 years of experience then reviewed all questions and categorized responses in one of the following 3 classes: Responses were classified into three categories by accuracy: appropriate (in line with clinical recommendations), inappropriate (contain errors), or unreliable (inconsistent). Descriptive statistical analysis was performed to compare ChatGPT responses against the standard and assessed them for accuracy and reproducibility.
Results
Of the total of 130 questions initially proposed, a total of 100 questions were deemed appropriate and were selected for the purpose of this study. When assessed against acceptable standard responses, ChatGPT responses were found to be appropriate in 91.5% of cases, inappropriate in 0.5%, and unreliable in 8%. Notably, out of 300 responses, 4% were irreproducible, meaning that they could not be consistently reproduced across multiple ChatGPT runs.
Conclusion
ChatGPT demonstrated promising accuracy in answering frequently asked questions related to hypertension. These findings highlight the potential for AI chatbots, such as ChatGPT, in offering valuable

information to patients regarding hypertension. Further research and refinement are needed to ensure the reliability and broader applicability of ChatGPT in the medical field.

Assessing the Accuracy and Reproducibility of Artificial Intelligence Chatbot in Answering Frequently Asked Questions Regarding Hypertension and its Associated Complications

Background
Neurosurgery residency is a fast-paced, time-consuming entity, which results in residents commonly approaching, if not violating the 80-hour workweek limit. This involves a complex structure with multiple hospitals, services, and clinic days, leading to challenges in creating fair call schedules. Historically, the manually-prepared scheduling system by the chief resident has been prone to biases, mistakes, and perceived unfairness. To address these issues, an automated and optimized neurosurgery scheduling system (AONSS) has been developed to reduce biases, accommodate resident requests, and optimize call variation, ultimately enhancing the educational experience by promoting diverse junior-senior-attending relationships. Of note, the national neurosurgery residency pool is reported to be ~18% female. 

Methods
AONSS was built using a combination of Perl and Visual Basic. The inaugural use of the AONSS occurred on July 1, 2021, in an optional manner, to the chiefs during the 2021-2022 academic year. An updated version of the program was implemented July 1, 2022 for the 2022-2023 academic year. Residents were surveyed using Google Forms with anonymous responses prior to its implementation and again as a follow up in July 2023. Objective primary outcome measure: senior and junior call variation (variance); Objective secondary outcome measures: variation in senior and junior Saturday call, senior and junior weekend call, senior-junior pairings, and resident-attending pairings. Subjective primary outcome measure: perceived satisfaction score based on 5-point Likert scale; Subjective secondary outcome measures: perceived fairness score (5-point Likert scale), perceived gender-bias (dichotomous) and perceived racial-bias (dichotomous).

Results
26 residents (28-39% female/year) were included in the study. AONSS was used for 6/13 blocks during the 2021-2022 academic year and 13/13 blocks for the 2022-2023 academic year. Overall call variation reduced by 70%. All other objective secondary measures have improved with AONSS. Monthly duty hours were reduced and less varied. Satisfaction scores improved from 21% reporting being somewhat satisfied or very satisfied to 90%. Fairness scores improved from 43% reporting being somewhat fair or very fair to 95%. Perception of gender bias decreased from 29% to 0%. No resident felt there was racial bias in either system.

Conclusion

Our newly developed automated scheduling system effectively reduces variation among calls in the complex UF neurosurgery residency, which in return, was found to increase residents’ satisfaction with their schedule, improve their perception of fairness with the schedule, and has completely removed the perception of sexual bias in a program that has a large percentage of females. Additionally, it was found to be associated with a decrease in duty hours. 


Automated and Optimized Neurosurgery Scheduling System Improves Resident Satisfaction

Background: Thrombolytic therapy is the standard of care in eligible patients presenting with acute ischemic stroke (AIS). Physicians are challenged to: 1) identify an AIS; 2) explain thrombolytic therapy; and 3) consent the patient and/or surrogate decision-maker efficiently and effectively. There is no consensus in clinician practice towards obtaining and documenting consent for thrombolytics in AIS. The objective of this study was to determine patient demographics and clinical characteristics that impact whether consent was documented for administration of thrombolytic therapy for AIS.

Methods: A multicenter mixed-methods cohort study included 442 patients who received thrombolytics for AIS. Demographics, including primary language, were collected from the electronic medical record (EMR). Code status, co-morbid dementia, stroke location, and initial NIHSS were also collected. Chi Square analysis was conducted to determine differences between two cohorts, patients who received thrombolysis and had documented consent and those who did not have documented consent in their EMR. Bivariate and multivariate regression were performed to determine associations with having documented consent. Thematic analysis was conducted on documented conversations to identify themes regarding consent for thrombolytics.

Results: The minority of patients (28.5%) had documented consent for thrombolysis in their EMR. In multivariate analysis, patients who were older (p<0.001), non-white (p=0.004), primary language other than English (p=0.03), co-morbid dementia (p<0.001), or had a DNR/partial code status (p=0.02) were more likely to have documented consent. Thematic analysis identified themes for documenting consent including risks and benefits, outcomes, and paternalism.

Conclusions: Documented consent was present in the minority of EMRs for patients treated with thrombolytics for AIS. Documented consent was more prevalent in populations with dementia, older age, primary language other than English, non-white, and code status. Qualitative analysis found effective consent was not documented in most cases. Given variation in consent documentation, it may be beneficial to develop standardized evidence-based language to guide these conversations.

Documentation of Consent for Thrombolytic Therapy in Acute Ischemic Stroke

Stress plays a central role in functioning for all life forms. As humans, we experience stress in a multitude of ways through various types of stimuli. Due to the constancy of stressors in our lives, the nervous system has learned to allosterically adapt to the stimuli. However, when the body cannot adapt to chronic stress it can have morphological and degenerative impacts on neuroanatomy and cognitive function that may or may not be reversible. In this literature review, our aim was to identify the specific neuroanatomical structures that were impacted most by the long-term effects of chronic stress. We also describe the impact the subsequent relationship between chronic stress and the morphological changes had on cognitive function, especially on medical students. We completed our analysis through a compilation of clinical trials and other systematic reviews. Our sources were assessed on credibility and referenced from Google Scholar, PubMed, Science Direct, and NCBI. The effects of various types of chronic stress had the most impact on neurogenesis and neuronal development, brain plasticity, and spatial learning and memory with association to the hippocampus. These structures had both reversible and non-reversible morphological alterations, which also had a direct impact on the brain’s cognitive abilities. While the results and impacts of chronic stress are still being conducted, future research may be able to further highlight why stressful stimuli can particularly impact these structures and the tangential impacts that it may have on related or adjacent structures. 
Introduction
With the hustle and bustle of daily life and the constant pressures of the evolving world, chronic stress has become a commonplace occurrence. Stress is categorized as any intrinsic or extrinsic stimulus that evokes a biological response, with the compensatory responses to these stimuli labeled as stress responses (1). Stressors can have an influence and impact on psychosocial health, physiological health, and even how the body maintains homeostasis (2). While the central nervous system is primed to produce integrated coping responses, it is the autonomic nervous system that engages in the fight, flight, or coping mechanisms that help the body adjust and adapt to the stressful stimuli that it may encounter (3). Some stress responses may be instantaneous, but if the stimuli persist over a long period of time, it is generally referred to as chronic stress (3). 
How an individual reacts to the strain that long-exposed stimuli pose can potentially affect both mental and physical health, with chronic stress having a more permanent effect on the body than short term stressors (4). Additionally, the time course of stress responses can be measured by the neuroendocrine and behavioral responses from the body, which can identify how destabilizing the stimulus makes the stress manageable or harmful (1). How the stressors are processed, in turn, can have a remarkable toll on the development and functioning of the brain and nervous systems. 
The brain plays a key integratory role in processing stimuli in the adaptation to stress. Stress and stimuli are information that stimulate behavioral and physiological responsiveness between the brain and other systemic functions (4). Beyond the acute responses to stress that life may pose, allostatic load is considered to be the integral feature of chronic stress that leads to degenerative effects (5). Allostatic load includes innate and adaptive processes that help to maintain homeostasis through mediator chemical messengers like adrenaline and cortisol (6). With allostatic load comes the heightened wear and tear on the central and peripheral nervous systems and even down to the cellular level (7, 8). This systematic review aims to focus on identifying key neurological structural changes that may be induced by the effects of chronic stress and put into context how these changes may, in turn, lead to changes in behavioral or mental health as well.


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How Comfortable are Medical Students with Interpreting Dermatologic Conditions During Didactic Training?

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