Emergency Medicine Applicants Research Articles

69 A Comprehensive Catalog of Emergency Medicine Applications of FDA-Regulated Artificial Intelligence-Enabled Products

69 A Comprehensive Catalog of Emergency Medicine Applications of FDA-Regulated Artificial Intelligence-Enabled Products

Total body weight estimation by 3D camera systems: Potential high-tech solutions for emergency medicine applications? A scoping review.

Weight estimation is required in adult patients when weight-based medication must be administered during emergency care, as measuring weight is often not possible. Inaccurate estimations may lead to inaccurate drug dosing, which may cause patient harm. High-tech 3D camera systems driven by artificial intelligence might be the solution to this problem. The aim of this review was to describe and evaluate the published literature on 3D camera weight estimation methods. A systematic literature search was performed for articles that studied the use of 3D camera systems for weight estimation in adults. Data on the study characteristics, the quality of the studies, the 3D camera methods evaluated, and the accuracy of the systems were extracted and evaluated. A total of 14 studies were included, published from 2012 to 2024. Most studies used Microsoft Kinect cameras, with various analytical approaches to weight estimation. The 3D camera systems often achieved a P10 of 90% (90% of estimates within 10% of actual weight), with all systems exceeding a P10 of 78%. The studies highlighted a significant potential for 3D camera systems to be suitable for use in emergency care. The 3D camera systems offer a promising method for weight estimation in emergency settings, potentially improving drug dosing accuracy and patient safety. Weight estimates were satisfactorily accurate, often exceeding the reported accuracy of existing weight estimation methods. Importantly, 3D camera systems possess characteristics that could make them very appropriate for use during emergency care. Future research should focus on developing and validating this methodology in larger studies with true external and clinical validation.

Application of Artificial Intelligence in Advanced Training and Education of Emergency Medicine Doctors: A Narrative Review

Emergency medicine (EM) demands continuous adaptation and refinement of training methodologies to equip healthcare professionals with the expertise to effectively manage complex and time-sensitive patient presentations. Artificial intelligence (AI), with its remarkable ability to process vast amounts of data, identify patterns, and make predictions, holds immense promise for enhancing the advanced training and education of EM physicians. This narrative review aims to discuss the potential of AI in transforming EM training and highlight the specific applications of AI in personalized learning, realistic simulations, data-driven decision support, and adaptive assessment, along with further exploring the benefits and challenges of AI-powered EM training. A comprehensive literature search was conducted using PubMed, MEDLINE, and Google Scholar to identify relevant studies focusing on AI applications in EM and EM training. The search terms included “artificial intelligence”, “emergency medicine”, “training”, “education”, “personalized learning”, “simulations”, “decision support”, and “assessment. Articles published in the past ten years were prioritized to ensure the inclusion of current advancements in the field. AI offers a plethora of opportunities to revolutionize EM training, including the following: Personalized learning: AI-powered systems can tailor educational content and pace to individual trainees’ needs, ensuring optimal instruction and knowledge acquisition. Realistic simulations: AI-powered simulations provide immersive experiences for trainees to practice clinical decision making under simulated pressure. Data-driven decision support: AI-powered systems analyze vast amounts of data to provide trainees with real-time recommendations and insights for informed clinical decisions. Adaptive assessment: AI-powered tools assess trainee progress dynamically, providing personalized feedback and identifying areas for improvement. Conclusions: AI integration into EM training holds immense promise for enhancing trainee learning and improving patient outcomes. By embracing AI, we can cultivate a new generation of EM physicians equipped to meet the ever-changing demands of this critical medical specialty.

Creation of a resident-led medical student education committee.

The Resident-Student Education Committee (RSEC) is a novel approach to integrate and expand medical student education within an emergency medicine (EM) residency at a large academic center. There is a paucity of literature on such programs and there is no documentation of longitudinal initiatives with residents serving as specialty-specific advisors to students throughout medical school. The goals of creating the RSEC were to expand and improve the student educational experiences in EM, strengthen the connection between students and EM residents, and foster resident career development through sustainable leadership and teaching opportunities. The RSEC was composed of three divisions: the Preclinical Division aimed to increase student exposure to EM through didactics, skill sessions, simulation, and shadowing; the Clinical Division intended to enhance the student experience during clinical EM rotations through simulation and skill sessions and resident-student socials; and the Mentoring Division focused on advising students applying into EM through informational panels and one-on-one resident mentorship. Outcome measures include students applying into EM residency, which saw an increase from 8.9% prior to the RSEC's creation in 2020 to 12.9% in 2023, despite a national decline in EM applicants. Survey data also indicates favorable student preclinical experiences and improved confidence in clinical skills. The RSEC model, with its structured approach, resident leadership, and clear objectives, presents a sustainable and replicable framework for other residency programs seeking to enhance medical student education and promote resident engagement in teaching. Future directions include expanding shadowing opportunities and procedural skills teaching, introducing career mentorship earlier in the medical education timeline, and tracking outcomes data for continuous assessment and improvement.

ChatGPT for use in technology-enhanced learning in anesthesiology and emergency medicine and potential clinical application of AI language models : Between hype and reality around artificial intelligence in medical use

The utilization of AI language models in education and academia is currently asubject of research, and applications in clinical settings are also being tested. Studies conducted by various research groups have demonstrated that language models can answer questions related to medical board examinations, and there are potential applications of these models in medical education as well. This study aims to investigate the extent to which current version language models prove effective for addressing medical inquiries, their potential utility in medical education, and the challenges that still exist in the functioning of AI language models. The program ChatGPT, based on GPT3.5, had to answer 1025 questions from the second part (M2) of the medical board examination. The study examined whether any errors and what types of errors occurred. Additionally, the language model was asked to generate essays on the learning objectives outlined in the standard curriculum for specialist training in anesthesiology and the supplementary qualification in emergency medicine. These essays were analyzed afterwards and checked for errors and anomalies. The findings indicated that ChatGPT was able to correctly answer the questions with an accuracy rate exceeding 69%, even when the questions included references to visual aids. This represented an improvement in the accuracy of answering board examination questions compared to astudy conducted in March; however, when it came to generating essays ahigh error rate was observed. Considering the current pace of ongoing improvements in AI language models, widespread clinical implementation, especially in emergency departments as well as emergency and intensive care medicine with the assistance of medical trainees, is aplausible scenario. These models can provide insights to support medical professionals in their work, without relying solely on the language model. Although the use of these models in education holds promise, it currently requires asignificant amount of supervision. Due to hallucinations caused by inadequate training environments for the language model, the generated texts might deviate from the current state of scientific knowledge. Direct deployment in patient care settings without permanent physician supervision does not yet appear to be achievable at present.

Advancements in Artificial Intelligence in Emergency Medicine in Taiwan: A Narrative Review.

The rapid progression of artificial intelligence (AI) in healthcare has greatly influenced emergency medicine, particularly in Taiwan-a nation celebrated for its technological innovation and advanced public healthcare. This narrative review examines the current status of AI applications in Taiwan's emergency medicine and highlights notable achievements and potential areas for growth. AI has wide capabilities encompass a broad range, including disease prediction, diagnostic imaging interpretation, and workflow enhancement. While the integration of AI presents promising advancements, it is not devoid of challenges. Concerns about the interpretability of AI models, the importance of dataset accuracy, the necessity for external validation, and ethical quandaries emphasize the need for a balanced approach. Regulatory oversight also plays a crucial role in ensuring the safe and effective deployment of AI tools in clinical settings. As its footprint continues to expand in medical education and other areas, addressing these challenges is imperative to harness the full potential of AI for transforming emergency medicine in Taiwan.

Analysis of Anonymous Student Narratives About Experiences with Emergency Medicine Residency Programs.

Academic emergency medicine (EM) communities have viewed anonymous online communities (AOC) such as Reddit or specialty-specific "applicant spreadsheets" as poor advising resources. Despite this, robust EM AOCs exist, with large user bases and heavy readership. Insights about applicants' authentic experiences can be critical for applicants and program leadership decision-making. To date, there are no EM studies to qualitatively assess EM AOC narratives during the application cycle. Our goal was to perform a qualitative analysis of students' EM program experiences through a publicly available AOC. This was a qualitative analysis of a publicly available, time-stamped, user-locked AOC dataset: "Official 2020-2021 Emergency Medicine Applicant Spreadsheet." We extracted and then de-identified all data from selected sub-sheets entitled "Virtual Interview Impressions" and "Rotation Impressions." Four investigators used constant comparative method to analyze the data inductively, and they subsequently met to generate common themes discussed by students. Preliminary thematic analysis was conducted on a random sample of 37/183 (20%) independent narratives to create the initial codebook. This was used and updated iteratively to analyze the entire narrative set consisting of 841 discrete statements. Finally, two unique codes were created to distinguish whether the identified sub-themes, or program attributes, were likely "modifiable" or "non-modifiable." We identified six major themes: living and working conditions; interpersonal relationships; learning experiences, postgraduate readiness, and online/virtual supplements. Common sub-themes included patient population (13%); resident personality (7%); program leadership personality (7%); relationship with faculty/leadership (6%); geography (4%); practice setting (4%); program reputation (4%), and postgraduate year-3 experiences (4%). Modifiable sub-themes outnumbered non-modifiable sub-themes, 60.7% to 39.3%. In this analysis of selected medical students' narratives in an AOC, the majority of identified themes represented topics that may serve as external feedback for EM residency programs and their clerkships. Selective use of AOCs may set a precedent for future program assessments by applicants and inform program leadership of important programmatic elements in the eyes of applicants. It elucidates important themes in their interactions or learning experiences with programs and creates opportunities for learner-centric program improvement.

Development of an Anticipatory Triage-Ranking Algorithm Using Dynamic Simulation of the Expected Time Course of Patients With Trauma: Modeling and Simulation Study.

In cases of terrorism, disasters, or mass casualty incidents, far-reaching life-and-death decisions about prioritizing patients are currently made using triage algorithms that focus solely on the patient's current health status rather than their prognosis, thus leaving a fatal gap of patients who are under- or overtriaged. The aim of this proof-of-concept study is to demonstrate a novel approach for triage that no longer classifies patients into triage categories but ranks their urgency according to the anticipated survival time without intervention. Using this approach, we aim to improve the prioritization of casualties by respecting individual injury patterns and vital signs, survival likelihoods, and the availability of rescue resources. We designed a mathematical model that allows dynamic simulation of the time course of a patient's vital parameters, depending on individual baseline vital signs and injury severity. The 2 variables were integrated using the well-established Revised Trauma Score (RTS) and the New Injury Severity Score (NISS). An artificial patient database of unique patients with trauma (N=82,277) was then generated and used for analysis of the time course modeling and triage classification. Comparative performance analysis of different triage algorithms was performed. In addition, we applied a sophisticated, state-of-the-art clustering method using the Gower distance to visualize patient cohorts at risk for mistriage. The proposed triage algorithm realistically modeled the time course of a patient's life, depending on injury severity and current vital parameters. Different casualties were ranked by their anticipated time course, reflecting their priority for treatment. Regarding the identification of patients at risk for mistriage, the model outperformed the Simple Triage And Rapid Treatment's triage algorithm but also exclusive stratification by the RTS or the NISS. Multidimensional analysis separated patients with similar patterns of injuries and vital parameters into clusters with different triage classifications. In this large-scale analysis, our algorithm confirmed the previously mentioned conclusions during simulation and descriptive analysis and underlined the significance of this novel approach to triage. The findings of this study suggest the feasibility and relevance of our model, which is unique in terms of its ranking system, prognosis outline, and time course anticipation. The proposed triage-ranking algorithm could offer an innovative triage method with a wide range of applications in prehospital, disaster, and emergency medicine, as well as simulation and research.

Impacts of United States Medical Licensing Examination Step 1 Scoring Change on Emergency Medicine Applicant Screening

BackgroundThe U.S. Medical Licensing Examination (USMLE) Step 1 has been used as a screening tool for residency selection. In February 2020, Step 1 numerical scoring changed to pass/fail. ObjectiveOur aim was to survey emergency medicine (EM) residency program attitudes towards the new Step 1 scoring change and to identify important applicant screening factors. MethodsA 16-question survey was distributed through the Council of Residency Directors in Emergency Medicine listserv from November 11 through December 31, 2020. Given the Step 1 scoring change, the survey questioned the importance of EM rotation grades, composite standardized letters of evaluation (cSLOEs), and individual standardized letters of evaluation, using a Likert scale. Descriptive statistics of demographic characteristics and selection factors were performed along with a regression analysis. ResultsOf the 107 respondents, 48% were program directors, 28% were assistant or associate program directors, 14% were clerkship directors, and 10% were in other roles. Sixty (55.6%) disagreed with pass/fail Step 1 scoring change and, of those, 82% believed that numerical scoring was a good screening tool. The cSLOEs, EM rotation grades, and interview were the most important selection factors. Residencies with 50 or more residents had 5.25 odds (95% CI 1.25–22.1; p = 0.0018) of agreeing with pass/fail scoring and those who ranked cSLOEs as the most important selection factor had 4.90 odds (95% CI 1.125–21.37; p = 0.0343) of agreeing with pass/fail scoring. ConclusionsMost EM programs disagree with pass/fail scoring of Step 1 and will most likely use Step 2 score as a screening tool. The cSLOEs, EM rotation grades, and interview are considered the most important selection factors.

The Aspects of Running Artificial Intelligence in Emergency Care; a Scoping Review.

Artificial Inteligence (AI) application in emergency medicine is subject to ethical and legal inconsistencies. The purposes of this study were to map the extent of AI applications in emergency medicine, to identify ethical issues related to the use of AI, and to propose an ethical framework for its use. A comprehensive literature collection was compiled through electronic databases/internet search engines (PubMed, Web of Science Platform, MEDLINE, Scopus, Google Scholar/Academia, and ERIC) and reference lists. We considered studies published between 1 January 2014 and 6 October 2022. Articles that did not self-classify as studies of an AI intervention, those that were not relevant to Emergency Departments (EDs), and articles that did not report outcomes or evaluations were excluded. Descriptive and thematic analyses of data extracted from the included articles were conducted. A total of 137 out of the 2175 citations in the original database were eligible for full-text evaluation. Of these articles, 47 were included in the scoping review and considered for theme extraction. This review covers seven main areas of AI techniques in emergency medicine: Machine Learning (ML) Algorithms (10.64%), prehospital emergency management (12.76%), triage, patient acuity and disposition of patients (19.15%), disease and condition prediction (23.40%), emergency department management (17.03%), the future impact of AI on Emergency Medical Services (EMS) (8.51%), and ethical issues (8.51%). There has been a rapid increase in AI research in emergency medicine in recent years. Several studies have demonstrated the potential of AI in diverse contexts, particularly when improving patient outcomes through predictive modelling. According to the synthesis of studies in our review, AI-based decision-making lacks transparency. This feature makes AI decision-making opaque.

Research of storable and ready-to-use artificial red blood cells (hemoglobin vesicles) for emergency medicine and otherclinical applications.

Hemoglobin (Hb) is the most abundant protein in blood, with concentration of about 12-15 g/dl. The highly concentrated Hb solution (35 g/dl) is compartmentalized in red blood cells (RBCs). Once Hb is released from RBCs by hemolysis during blood circulation, it induces renal and cardiovascular toxicities. To date, hemoglobin-based oxygen carriers of various types have been developed as blood substitutes to mitigate the Hb toxicities. One method is Hb encapsulation in phospholipid vesicles (liposomes). Although the Hb toxicity can be shielded, it is equally important to ensure the biocompatibility of the liposomal membrane. We have developed Hb-vesicles (HbV). A new encapsulation method using a rotation-revolution mixer which enabled efficient production of HbV with a high yield has considerably facilitated R&D of HbV. Along with our academic consortium, we have studied the preclinical safety and efficacy of HbV extensively as a transfusion alternative, and finally conducted a phase I clinical trial. Moreover, carbonyl-HbV and met-HbV are developed respectively for an anti-inflammatory and anti-oxidative agent and an antidote for poisons. This review paper specifically presents past trials of liposome encapsulated Hb, biocompatible lipid bilayer membranes, and efficient HbV preparation methods, in addition to potential clinical applications of HbV based on results of our in vivo studies.

How do emergency medicine applicants evaluate residency programs in the post-COVID-19 era?

Pandemic disruptions to interviews and away rotations led applicants to rely on alternative sources of information about residency programs. We sought to compare program characteristics important to emergency medicine (EM)-bound applicants from before and after the pandemic. We also investigated the sources of information pandemic impacted applicants used during residency recruitment. This was a multi-institutional, cross-sectional survey of applicants to EM residency programs. We asked applicants about important factors in choosing a program and compared their response to results from 2019 National Residency Match Program. We also asked about alternative information sources used during this time of restricted access to programs of interest. We surveyed 414 applicants from 40 medical schools and attained a response rate of 38.4%. Compared to 2019 applicants, our respondents identified morale of residents and quality of faculty as important factors in choosing a residency. Our subjects cited websites of the program and hospital affiliate, current residents, faculty/mentor advice, Reddit, and Doximity as sources of program information. Compared to 2019, our EM-bound applicants who, because of the pandemic, were unable to visit programs of interest valued resident morale and faculty quality as factors in choosing a residency program. Without in-person visits, our subjects also had to use both virtual resources (e.g., websites) and traditional sources (e.g., mentor advice) to investigate a program's culture, reputation, and diversity and inclusion. Residency programs should monitor their online presence now that this has become an alternative source of information for applicants during curtailment of in-person visits.

How do people think about the implementation of speech and video recognition technology in emergency medical practice?

BackgroundRecently, speech and video information recognition technology (SVRT) has developed rapidly. Introducing SVRT into the emergency medical practice process may lead to improvements in health care. The purpose of this study was to evaluate the level of acceptance of SVRT among patients, caregivers and emergency medical staff.MethodsStructured questionnaires were developed for the patient or caregiver group and the emergency medical staff group. The survey was performed in one tertiary academic hospital emergency department. Questions were optimized for each specific group, and responses were provided mostly using Likert 5-scales. Additional multivariable logistic regression analyses for the whole cohort and subgroups were conducted to calculate odds ratios (OR) and confidence intervals (CI) to examine the association between individual characteristics and SVRT acceptance.ResultsOf 264 participants, respondents demonstrated a positive attitude and acceptance toward SVRT and artificial intelligence (AI) in future; 179 (67.8%) for video recordings, and 190 (72.0%) for speech recordings. A multivariable logistic regression model revealed that several factors were associated with acceptance of SVRT in emergency medical practice: belief in health care improvement by signal analysis technology (OR, 95% CIs: 2.48 (1.15–5.42)) and AI (OR, 95% CIs: 1.70 (0.91–3.17)), reliability of AI application in emergency medicine (OR, 95% CIs: 2.36 (1.28–4.35)) and the security of personal information (OR, 95% CIs: 1.98 (1.10–3.63)).ConclusionA high level of acceptance toward SVRT has been shown in patients or caregivers, and it also appears to be associated with positive attitudes toward new technology, AI and security of personal information.

Work environment in emergency medicine needs to be improved

Background: Emergency medicine (EM) is a novel specialty in Denmark. So far there are only 111 board certified specialists in EM in Denmark ~5 per Emergency department (ED). We hypothesize that stressful ED work environment may impact EM recruitment. The aim of our analysis was to evaluate the rate of applicants for EM junior and senior residency and relate it to ED patient complaint rate and work environment.
 Methods: We evaluated EM junior residency applications; and we calculated the rate of unfilled versus posted EM senior residency appointments In Denmark in 2020. As received by The Danish Agency for Patient Complaints we compared complaints from patients received over a 3-year period for EM, Respiratory medicine (RM), Cardiology (Card) and Endocrinology (Endo). One-way analysis of variance was performed to assess overall significance which was followed by a Tukey studentized range test procedure to locate the possible differences. We anonymously asked affiliated EM junior residents to qualify the reason for their application and whether they intend to apply for senior residency.Results We found a surplus of EM junior residency applications. Thus, positions were easily filled. Conversely unfilled senior EM residency appointments in Denmark amounted to 31 out of 52 posted appointments, meaning that 59% of EM senior residency appointments were unfilled. Patient complaints amounted to (means±SEM) EM 333±55; RM 88±9; Card 113±17; Endo 58±8, meaning a 3 to 5fold higher patient complaint rate in EM (p < 0.0006). There were no statistical differences in patient complaints among RM, Card and Endo. Among 8 EM junior residents 63% expressed interest in an EM career as the reason for their application but following EM experience none intended to apply for senior residency.
 Conclusion: More than half of posted senior residency positions in EM remain vacant. Patient complaints are significantly frequent in EM compared to other medical specialties. Junior residents are reluctant to pursue senior residency. Stressful ED working environment needs to be improved to facilitate EM recruitment.

Away Rotation Applications in Emergency Medicine: Medical Student Behaviors, Outcomes, and Stressors

Completing an emergency medicine (EM) away rotation is integral to matching successfully into an EM residency program. The demand for EM away rotations (ARs) drives students to submit numerous applications without evidence-based recommendations to guide stakeholders on the approach or number to submit. We conducted a survey study of EM-bound fourth-year medical students to gain insight into their AR application experiences, outcomes, and perceptions. We distributed a 40-item questionnaire to EM applicants in Fall 2018 via e-mail through the Clerkship Directors in Emergency Medicine, Council of Residency Directors in EM, and Emergency Medicine Residents' Association listservs. Responses were evaluated using quantitative and qualitative analysis. Primary outcomes were the number of AR applications submitted and AR offers received by students. Secondary outcomes were students' self-assessment of their competitiveness, differences in AR application numbers by degree type, sources of student advising, and student perceptions of the AR application process. There were 253 respondents, consisting of 192 allopathic (MD) and 61 osteopathic (DO) medical students, who met the inclusion criteria, representing about 10% of the applicant pool. On average, students submitted 13.97 applications (95% confidence interval [CI] 11.59-16.35), received 3.25 offers (95% CI 3.01-3.49), and accepted 2.22 offers (95% CI 2.08-2.36). DO candidates submitted twice as many applications as MD candidates while experiencing a similar rate of offers received. Peer influence (n=154, 61%), peer online advising networks (n=83, 33%), and self-assessment (n=114, 45%) were the most often reported causes of increased applications; cost (n=104, 41%) and geographic limitations (n=114, 45%) were the most often reported causes of decreased applications. Open-response analysis revealed frustration with lack of standardization (n=44, 29.5%), insufficient transparency on available positions (n=37, 24.8%), limited communication (n=30, 20.1%), and cost (n=12, 8.1%). This study showed that, as a whole, students received one away rotation offer for every four to five applications submitted. It clarified factors contributing to increased EM away rotation application submissions and associated stressors inherent in the application experience. Our findings offer insights to inform advising recommendations. They also suggest that stakeholders consider standardizing the process and improve communication over spot availability and application status.

Spherical Sampler Probes Enhance the Robustness of Ambient Ionization Mass Spectrometry for Rapid Drugs Screening.

Ambient ionization mass spectrometry has become one of the most promising approaches for rapid and high-throughput screening of small molecules in complex biological matrices for emergency medicine, forensics, and food and agriculture applications. The simple procedures for sample collection and ionization without additional pretreatment are vital in these fields. Many efforts have been devoted to modifying various ambient ionization techniques to simplify the procedures and improve the robustness and sensitivity of the methods. Here, we demonstrate the implementation of rigid spherical sampler probes to improve the robustness of touch spray ionization mass spectrometry. The sphericity of the probes increases the stability of the cone-jet mode of electrospray, reduces the requirements for fine positioning of a sampler in the ion source, and decreases the possibility of corona discharge occurrence. The utilization of spherical sampler probes allows fast, non-invasive sampling, followed by rapid analysis for various drugs of different chemical classes in complex biological matrices, such as the whole blood or sebum collected from the skin surface. The linearity of the analytical signal response from drug concentration confirms the possibility of creating a simple semiquantitative method for small molecules monitoring using spherical sampler probes.

“Must Love Rock Climbing?” Emergency Medicine Applicants’ Hobbies from Two Academic Institutions

Background: Hobbies may be used on the residency application to communicate similarity or uniqueness to other members of their desired specialty. Objectives: This study aimed to determine the most common hobbies among emergency medicine (EM) applicants and whether they differ from the reported hobbies of other applicants. Methods: This was a two-center retrospective application review study at large U.S. midwestern academic institutions with a 3- and a 4-year EM residency program. Data from the “hobbies and interests” field of the Electronic Residency Application Service (ERAS) application from 2015 - 2019 were reviewed. Hobbies data were individually coded by two independent physician coders into 19 defined hobby types. EM applicants were compared to plastic surgery and psychiatry applicants. Results: A total of 2675 individual hobbies were initially identified, with strong agreement between raters (κ = 0.92). The most commonly listed EM hobbies were sports (25.0%), outdoor (19.3%), and fitness (9.6%). Sports and outdoor hobbies were also listed most frequently by applicants to psychiatry and plastic surgery. While there were several significant differences between EM and other specialties, these differences were minimal overall. Conclusions: Active hobbies appeared most prominently for all applicants. Less stereotypical EM hobbies, including literary arts and cooking, were also frequently noted, suggesting that applicants may have motivations beyond communicating similarity when listing hobbies.

Artificial intelligence and machine learning in emergency medicine: a narrative review.

AimThe emergence and evolution of artificial intelligence (AI) has generated increasing interest in machine learning applications for health care. Specifically, researchers are grasping the potential of machine learning solutions to enhance the quality of care in emergency medicine.MethodsWe undertook a narrative review of published works on machine learning applications in emergency medicine and provide a synopsis of recent developments.ResultsThis review describes fundamental concepts of machine learning and presents clinical applications for triage, risk stratification specific to disease, medical imaging, and emergency department operations. Additionally, we consider how machine learning models could contribute to the improvement of causal inference in medicine, and to conclude, we discuss barriers to safe implementation of AI.ConclusionWe intend that this review serves as an introduction to AI and machine learning in emergency medicine.

Smartphone-based fundus imaging: applications and adapters

Smartphone-based fundus imaging (SBFI) is an innovative and low-cost alternative for color fundus photography. Since the first reports on this topic more than 10years ago alarge number of studies on different adapters and clinical applications have been published. The aim of this review article is to provide an overview on the development of SBFI and adapters and clinical applications published so far. Aliterature search was performed using the MEDLINE and Science Citation Index Expanded databases without time restrictions. Overall, 11 adapters were included and compared in terms of exemplary image material, field of view, acquisition costs, weight, software, application range, smartphone compatibility and certification. Previously published SBFI applications are screening for diabetic retinopathy, glaucoma and retinopathy of prematurity as well as the application in emergency medicine, pediatrics and medical education/teaching. Image quality of conventional retinal cameras is in general superior to SBFI. First approaches on automatic detection of diabetic retinopathy through SBFI are promising and the use of automatic image processing algorithms enables the generation of wide-field image montages. SBFI is aversatile, mobile, low-cost alternative to conventional equipment for color fundus photography. In addition, it facilitates the delegation of ophthalmological examinations to assistance personnel in telemedical settings, could simplify retinal documentation, improve teaching, and improve ophthalmological care, particularly in countries with low and middle incomes.

Lower-Third Standardized Letters of Evaluation in Emergency Medicine: Does Gender Make a Difference in Match Outcome?

ObjectiveThe purpose of this study was to determine whether gender influences the likelihood of receiving a lower-third global assessment (GA) on the standardized letter of evaluation (SLOE) submitted as part of the emergency medicine (EM) application process as well as the impact of gender on ultimate match outcomes for applicants receiving a lower-third GA ranking. Our hypothesis was that female applicants with a lower-third GA ranking have a higher risk of not matching.MethodsWe conducted a retrospective cohort study evaluating U.S.-based allopathic applicants to a single EM residency program in the Mid-Atlantic region during the 2017-2018 and 2018-2019 match cycles. GA SLOE rankings and gender for all applicants were extracted and compared to the National Resident Matching Program (NRMP) data for each applicant on match outcome. Comparative analyses were conducted between gender and SLOE GA rankings in order to obtain an odds ratio (OR) of gender and match outcomes.ResultsA total of 2,017 SLOEs were reviewed from 798 applicants in the 2018 and 2019 EM match cycles. Overall, 716 (90%) applicants successfully matched in EM, with 82 (10%) applicants failing to match into EM; 277 students had at least one lower-third GA ranking. For all applicants, having at least one lower-third GA ranking was associated with a significant risk of not matching (OR: 0.20; 95% CI: 0.12-0.34). Of the 277 students with at least one lower-third GA ranking, 85 (31%) were female and 192 (69%) were male. Of the female applicants with a lower-third GA ranking, 15 (18%) failed to match in EM, and 39 (20%) of the males failed to match in EM. For applicants with a lower-third GA ranking, female gender alone was not associated with a significantly increased risk of not matching (OR: 1.18; 95% CI: 0.61-2.21).ConclusionsFemale applicants receive a lower-third GA ranking less frequently than their male counterparts. One or more lower-third rankings on the GA significantly reduced an applicant’s chances of matching into an EM program. For those with a lower-third GA ranking, female gender alone does not significantly increase the risk of not matching into EM.