United States Medical Licensing Examination Research Articles

United States Medical Licensing Examination Performance and Outcomes of Patient Care.

United States Medical Licensing Examination Performance and Outcomes of Patient Care.

Gender differences in learning and study strategies impact medical students’ preclinical and USMLE step 1 examination performance

BackgroundEvaluation of students’ learning strategies can enhance academic support. Few studies have investigated differences in learning strategies between male and female students as well as their impact on United States Medical Licensing Examination® (USMLE) Step 1 and preclinical performance.MethodsThe Learning and Study Strategies Inventory (LASSI) was administered to the classes of 2019–2024 (female (n = 350) and male (n = 262)). Students’ performance on preclinical first-year (M1) courses, preclinical second-year (M2) courses, and USMLE Step 1 was recorded. An independent t-test evaluated differences between females and males on each LASSI scale. A Pearson product moment correlation determined which LASSI scales correlated with preclinical performance and USMLE Step 1 examinations.ResultsOf the 10 LASSI scales, Anxiety, Attention, Information Processing, Selecting Main Idea, Test Strategies and Using Academic Resources showed significant differences between genders. Females reported higher levels of Anxiety (p < 0.001), which significantly influenced their performance. While males and females scored similarly in Concentration, Motivation, and Time Management, these scales were significant predictors of performance variation in females. Test Strategies was the largest contributor to performance variation for all students, regardless of gender.ConclusionGender differences in learning influence performance on STEP1. Consideration of this study’s results will allow for targeted interventions for academic success.

Academic performance of students in an accelerated medical pathway

ABSTRACT Accelerated medical school curricula, such as three-year programs, have gained attention in recent years but studies evaluating their impact are still scarce. This study examines the Fully Integrated Readiness for Service Training (FIRST) program, a three-year accelerated pathway, to assess its impact on students’ academic performance preparedness for residency. In this observational study, we compared the academic outcomes of FIRST program students to traditional four-year curriculum students from 2018 to 2023. We analyzed multiple metrics, including exam performance (United States Medical Licensing Examination Step scores, shelf exam scores, and pre-clinical course scores) and clinical performance scores during the application and individualization phases. Analysis of Variance was used to examine the effect of accelerated pathway program experience relative to traditional 4-year medical school curriculum on the learning outcomes. FIRST program students were on average 1.5 years younger upon graduation than their traditional peers. While FIRST program students scored slightly lower on Step 2 Clinical Knowledge (CK), they exhibited no significant differences in other exam scores or clinical performance relative to the traditional students. Notably, FIRST students achieved equivalent clinical performance ratings during critical clerkships and rotations. Our findings suggest that a three-year medical school curriculum can effectively prepare students for residency and produce graduates with comparable medical knowledge and clinical skills, offering potential benefits in terms of financial relief and personal well-being for medical students.

DO seniors and IMGs have lower match probabilities than MD seniors after adjusting for specialty choice and USMLE Step 1 score.

It is unknown if US residency applicants of different educational backgrounds (US allopathic [MD], Doctor of Osteopathic Medicine [DO], and international medical graduates [IMG]) but comparable academic performance have similar match success. Our objective was to compare match probabilities between applicant types after adjusting for specialty choice and United States Medical Licensing Examination (USMLE) Step 1 scores. We performed a secondary analysis of published data in National Resident Matching Program (NRMP) reports from 2016, 2018, 2020, and 2022 for US MD seniors, DO seniors, and IMGs (US citizens and non-US citizens). We examined the 10 specialties with the most available spots in 2022. Average marginal effects from a multiple variable logistic regression model were utilized to estimate each non-MD senior applicant type's probability of matching into their preferred specialty compared to MD seniors adjusting for specialty choice, Step 1 score, and match year. Each non-MD applicant type had a lower adjusted percent difference in matching to their preferred specialty than MD seniors,-7.1 % (95 % confidence interval [CI],-11.3 to-2.9) for DO seniors,-45.6 % (-50.6 to-40.5) for US IMGs, and-56.6 % (-61.5 to-51.6) for non-US IMGs. Similarly, each non-MD applicant type had a lower adjusted percent difference in matching than MD seniors across almost all Step 1 score ranges, except for DO seniors with Step 1 scores <200 (-2.0 % [-9.5 to 5.5]). After adjusting for specialty choice, Step 1 score, and match year, non-US MD applicants had lower probabilities of matching into their preferred specialties than their US MD colleagues.

Medical students' perspective on the United States Medical Licensing Examination (USMLE) Step 1 transition to Pass/Fail

Background The transition of the United States Medical Licensing Exam: Step 1 to Pass/Fail (P/F), from scored, caused uncertainty about students’ preparedness and wellbeing related to the exam. Comparison of study behavior and results, before and after the P/F transition can provide insights for the medical curriculum and student support. Methods Data from four cohorts of second-year medical students (Class of 2022–25, N = 204) were collected from their dedicated Step 1 self-study block. Student study regiments, aggregate practice test results, Step 1 pass rates and post-block self-reported surveys were analyzed. Results Analysis of practice exam averages across the four student cohorts demonstrates a potentially slower and less rigorous start to Step 1 self-studying during the dedicated preparation block for the cohorts that took the P/F Step 1 exam format as compared to the previous cohorts that prepared for a scored Step 1 exam. Similarly, self-reported study regiments decreased in the median number of hours/day and number of weeks of study for the cohorts with P/F Step 1 exam. There was also a slight shift in the type of study resources used, between the two groups, with the scored group using more traditional board preparation resources. Conclusions The P/F transition of the Step 1 exam may lead to reduced student preparedness and may require adjustments in the resources and support provided by institutions.

Can USMLE and COMLEX-USA Scores Predict At-Risk Emergency Medicine Residents' Performance on In-Training Examinations?

The United States Medical Licensing Examination (USMLE) and Comprehensive Osteopathic Medical Licensing Examination (COMLEX) scores are standard methods used to determine residency candidates' medical knowledge. The authors were interested in using the USMLE and COMLEX part 2 scores in our emergency medicine (EM) residency program to identify at-risk residents who may have difficulty on the in-training exam (ITE) and to determine the cutoff values under which an intern could be given an individualized study plan to ensure medical knowledge competency. The authors abstracted the USMLE and COMLEX part 2 scores and the American Board of Emergency Medicine (ABEM) ITE scores for a cohort of first-year EM residents graduating years 2010-2022, converting raw scores to percentiles, and compared part 2 and ABEM ITE scores with Pearson's correlation, a Bland-Altman analysis of bias and 95% limits of agreement, and ROC analysis to determine optimal the cut-off values for predicting ABEM ITE < 50th percentile and the estimated test characteristics. Scores were available for 152 residents, including 93 USMLE and 88 COMLEX exams. The correlations between part 2 scores and ABEM ITE were r = 0.36 (95%CI: 0.17, 0.52; p < 0.001) for USMLE and r = 0.50 (95%CI: 0.33, 0.64; p < 0.001) for COMLEX. Bias and limits of agreement for both part 2 scores were -14 ±63% for USMLE and 13 ± 50% for COMLEX in predicting the ABEM ITE scores. USMLE < 37th percentile and COMLEX < 53rd percentile identified 42% (N = 39) and 27% (N = 24) of EM residents, respectively, as at risk, with a sensitivity of 61% and 49% and specificity of 71% and 92%, respectively. USMLE and COMLEX part 2 scores have a very limited role in identifying those at risk of low ITE performance, suggesting that other factors should be considered to identify interns in need of medical knowledge remediation.

The Impact of United States Medical Licensing Examination Step 1 Transitioning To Pass/Fail on Medical Student Perception of Research Needed to Match Into One's Preferred Specialty.

Purpose To evaluate how the transition of United States Medical Licensing Examination (USMLE) Step 1 to a pass/fail scoring influenced medical student perceptions of the importance of research required to match into their preferred residency specialty. Methods A 14-item survey was distributed by e-mail to medical students at one medical school in the southeastern United States in November of 2021. Responses were compared between medical students taking USMLE Step 1 pass/fail in the future and medical students taking USMLE Step 1 for a three-digit score. Results A total of 168 medical students responded to the survey with 98 respondents who planned on taking USMLE Step 1 pass/fail (45 first-year medical students (MS1) and 53 MS2) and 70 respondents who took USMLE Step 1 for a numerical score (37 MS3and 33 MS4). There were no differences in how each cohort scored the level of importance of research in matching into their preferred residency specialty (p=0.10); however, those taking USMLE Step 1 pass/fail believe an average of 4.6 research experiences are necessary to match into their preferred residency, compared to only 3.4 research experiences for those who took it for a numerical score (p=0.04). Conclusion No statistically significant difference in the perceived importance of research in matching into one's preferred residency specialty was found between cohorts. However, the pass/fail cohort believes they will need more research experiences to match their chosen specialty than the numerical score cohort. Results could indicate that students participate in more research and extracurricular activities to be more competitive for residency applications.

Matching Against Men: 5 Years of Residency Match Data Show Disparities Still Exist.

Despite progress toward equal representation by sex in medical practice, women remain underrepresented in many specialties. This study sought to examine the current state of gender equality among recently graduated doctors in multiple specialties. Deidentified demographics, standardized examination scores, and Match results were gathered for 829 graduates. Participants were selected from an allopathic medical school between 2016 and 2020. Nineteen students (2.29%) were excluded from the study. Descriptive statistics were calculated, and χ2 tests for independence were used to compare proportions between reported sex and specialty and program Match results. One-way analysis of variance was then performed to test for differences in US Medical Licensing Examination Step 1 and Step 2 scores between sexes. P < 0.05 was considered statistically significant. Of the 829 individuals studied, 44.6% were women. A significantly smaller proportion of women matched into the most competitive specialties, despite no significant difference in US Medical Licensing Examination Step 1 scores between sexes. Furthermore, there was an overall significant trend of women matching into more competitive programs for any given specialty. In this study, we found that men matched into more highly competitive specialties, whereas women matched into more competitive residency program locations. Further research is needed to determine why women matched into specific specialties at lower rates than their male peers and seek to understand how sex affects the narrative of specialty choice.

Racial and Ethnic Disparities in Situational Judgment Testing Among Applicants to an Anesthesiology Residency Program

Background The Computer-Based Assessment for Sampling Personal Characteristics (CASPer) is a situational judgment test (SJT) that assesses noncognitive skills like professionalism, communication, and empathy. There are no reports of the effects of race/ethnicity and sex on CASPer scores among residency applicants. Objective We examined the effects of race/ethnicity, sex, and United States vs international medical school attendance on CASPer performance. Methods Our anesthesiology residency program required all applicants for the 2021-2022 Match cycle to complete an online video and text-based SJT (CASPer). We compared these results, reported as z-scores, with self-identified race/ethnicity, sex, United States vs international medical school attendance, and United States Medical Licensing Examination (USMLE) Step 1 scores. Results Of the 1245 applicants who completed CASPer, 783 identified as male. The racial/ethnic distribution was 512 White, 412 Asian, 106 Black, 126 Hispanic, and 89 Other/No Answer. CASPer z-scores did not differ by sex. White candidates scored higher than Black (0.18 vs -0.57, P&amp;lt;.001) and Hispanic (0.18 vs -0.52, P&amp;lt;.001) candidates. Applicants attending US medical schools scored higher than those attending international medical schools (z-scores: 0.15 vs -0.68, P&amp;lt;.001). There was no correlation between CASPer z-scores and USMLE Step 1 scores. Conclusions Our results suggest that CASPer scores favor White applicants over Black and Hispanic ones and applicants attending US medical schools over those attending international medical schools.

Program Directors' Perspectives on Residency Applications in the Post‒United States Medical Licensing Examination Step 1 Era: A Case for Standardized Letters of Recommendation?

The United States Medical Licensing Examination (USMLE) Step 1 test evolved into a key metric utilized by program directors (PDs) in assessing candidates for residency. The transition to a USMLE Step 1 binary pass/fail scoring system has resulted in a loss of an important objective assessment. With national movements toward pass/fail systems for clerkship grading and trends toward abandonment of class ranking, assessing residency applications has become increasingly challenging. The Society of Academic Urologists convened a task force to, in part, assess the perspectives of urology PDs regarding the importance of various aspects of a residency application for predicting clinical performance. An anonymous survey was disseminated to all urology PDs in the US. Perspectives on 11 potential application predictors of clinical performance and demographics were recorded. Descriptive statistics characterized PD responses. Friedman test and pairwise Wilcoxon tests were used to evaluate the relative ranks assigned to application elements by PDs. There was a 60.5% response rate (89/147). Letters of recommendation (LORs) were ranked as the most important predictor, with a mean rank of 2.39, median of 2 (IQR 1-3). Clerkship grades and USMLE Step 1 were comparable and ranked second. Medical school reputation ranked the lowest. There was significant subjective heterogeneity among categories; however, this was less so for LORs, which predominated as the most important factor among application elements (P < .001). To our knowledge, this is the largest sample size assessing PD perspectives on application factors that predict clinical performance. The second (clerkship grades) and third (USLME Step 1) most important factors moving toward binary pass/fail systems create an opportunity for actionable change to improve assessment objectivity. Our data demonstrate LORs to be the most important factor of residency applications, making a compelling argument for moving toward a standardized LOR to maximize this tool, mitigate bias, and improve interreviewer reliability.

A Comparison Between GPT-3.5, GPT-4, and GPT-4V: Can the Large Language Model (ChatGPT) Pass the Japanese Board of Orthopaedic Surgery Examination?

Introduction Recently, large-scale language models, such as ChatGPT (OpenAI, San Francisco, CA), have evolved. Thesemodels are designed to think and act like humans and possess a broad range of specialized knowledge. GPT-3.5 was reported to be at a level of passing the United States Medical Licensing Examination. Its capabilities continue to evolve, and in October 2023, GPT-4V became available as a model capable of image recognition. Therefore, it is important to know the current performance of these models because they will be soon incorporated into medical practice. We aimed to evaluate the performance of ChatGPT in the field of orthopedic surgery. Methods We used three years' worth of Japanese Board of Orthopaedic Surgery Examinations (JBOSE) conducted in 2021, 2022, and 2023. Questions and their multiple-choice answers were used in their original Japanese form, as was the official examination rubric. We inputted these questions into three versions of ChatGPT: GPT-3.5, GPT-4, and GPT-4V. For image-based questions, we inputted only textual statements for GPT-3.5 and GPT-4, and both image and textual statements for GPT-4V. As the minimum scoring rate acquired to pass is not officially disclosed, it was calculated using publicly available data. Results The estimated minimum scoring rate acquired to pass was calculated as 50.1% (43.7-53.8%). For GPT-4, even when answering all questions, including the image-based ones, the percentage of correct answers was 59% (55-61%) and GPT-4 was able to achieve the passing line. When excluding image-based questions, the score reached 67% (63-73%). For GPT-3.5, the percentage was limited to 30% (28-32%), and this version could not pass the examination. There was a significant difference in the performance between GPT-4 and GPT-3.5 (p < 0.001). For image-based questions, the percentage of correct answers was 25% in GPT-3.5, 38% in GPT-4, and 38% in GPT-4V. There was no significant difference in the performance for image-based questions between GPT-4 and GPT-4V. Conclusions ChatGPT had enough performance to pass the orthopedic specialist examination. After adding further training data such as images, ChatGPT is expected to be applied to the orthopedics field.

The performance of large language models in intercollegiate Membership of the Royal College of Surgeons examination.

Large language models (LLM), such as Chat Generative Pre-trained Transformer (ChatGPT) and Bard utilise deep learning algorithms that have been trained on a massive data set of text and code to generate human-like responses. Several studies have demonstrated satisfactory performance on postgraduate examinations, including the United States Medical Licensing Examination. We aimed to evaluate artificial intelligence performance in Part A of the intercollegiate Membership of the Royal College of Surgeons (MRCS) examination. The MRCS mock examination from Pastest, a commonly used question bank for examinees, was used to assess the performance of three LLMs: GPT-3.5, GPT 4.0 and Bard. Three hundred mock questions were input into the three LLMs, and the responses provided by the LLMs were recorded and analysed. The pass mark was set at 70%. The overall accuracies for GPT-3.5, GPT 4.0 and Bard were 67.33%, 71.67% and 65.67%, respectively (p = 0.27). The performances of GPT-3.5, GPT 4.0 and Bard in Applied Basic Sciences were 68.89%, 72.78% and 63.33% (p = 0.15), respectively. Furthermore, the three LLMs obtained correct answers in 65.00%, 70.00% and 69.17% of the Principles of Surgery in General questions (p = 0.67). There were no differences in performance in the overall and subcategories among the three LLMs. Our findings demonstrated satisfactory performance for all three LLMs in the MRCS Part A examination, with GPT 4.0 the only LLM that achieved the pass mark set.

Evaluating Urology Program Directors' Perception on Resident Application Parameters Following the Transition of USMLE Step 1 to Pass/Fail

ObjectiveTo investigate how the shift of the United States Medical Licensing Examination (USMLE) Step 1 to a Pass/Fail (P/F) scoring system impacts the perceptions of Urology Program Directors (PDs) on evaluating urology residency applicants. Methods and MaterialsA cross-sectional survey was sent to 117 PDs, including questions about program characteristics, perceptions of shelf scores and medical school rank post-transition, beliefs about the predictive value of Step 1 and Step 2 Clinical Knowledge (CK) scores for board success and residency performance, and changes in applicant parameter ranking. Results45 PDs (38% response rate) participated. Notably, 49% favored releasing quantitative clerkship grades, and 71% valued medical school rank more. Opinions on Step 1 scores' correlation with board success were split (49% agreed), and 44% endorsed Step 2 CK scores' connection to board performance. As predictors of good residents, only 9% and 22% considered Step 1 and Step 2 CK scores, respectively, indicative. Clerkship grades and Urology rotation recommendation letters-maintained significance, while research experience gained importance. Step 2 CK scores' importance rose but didn’t match Step 1 scores' previous significance. ConclusionThe transition to Pass/Fail (P/F) for USMLE Step 1 adds intricacies to urology residency selection, exposing Program Directors' uncertainties regarding clerkship grades and the relevance of medical school rank. This research underscores the dynamic nature of urology residency admissions, emphasizing the increasing importance of research in evaluating applicants and a diminishing emphasis on volunteering and leadership.

Performance of Progressive Generations of GPT on an Exam Designed for Certifying Physicians as Certified Clinical Densitometrists

Background: Artificial intelligence (AI) large language models (LLMs) such as ChatGPT have demonstrated the ability to pass standardized exams. These models are not trained for a specific task, but instead trained to predict sequences of text from large corpora of documents sourced from the internet. It has been shown that even models trained on this general task can pass exams in a variety of domain-specific fields, including the United States Medical Licensing Examination. We asked if large language models would perform as well on a much narrower subdomain tests designed for medical specialists. Furthermore, we wanted to better understand how progressive generations of GPT (generative pre-trained transformer) models may be evolving in the completeness and sophistication of their responses even while generational training remains general. In this study, we evaluated the performance of two versions of GPT (GPT 3 and 4) on their ability to pass the certification exam given to physicians to work as osteoporosis specialists and become a certified clinical densitometrists. The CCD exam has a possible score range of 150 to 400. To pass, you need a score of 300.Methods: A 100-question multiple-choice practice exam was obtained from a 3rd party exam preparation website that mimics the accredited certification tests given by the ISCD (International Society for Clinical Densitometry). The exam was administered to two versions of GPT, the free version (GPT Playground) and ChatGPT+, which are based on GPT-3 and GPT-4, respectively (OpenAI, San Francisco, CA). The systems were prompted with the exam questions verbatim. If the response was purely textual and did not specify which of the multiple-choice answers to select, the authors matched the text to the closest answer. Each exam was graded and an estimated ISCD score was provided from the exam website. In addition, each response was evaluated by a rheumatologist CCD and ranked for accuracy using a 5-level scale. The two GPT versions were compared in terms of response accuracy and length.Results: The average response length was 11.6 ±19 words for GPT-3 and 50.0±43.6 words for GPT-4. GPT-3 answered 62 questions correctly resulting in a failing ISCD score of 289. However, GPT-4 answered 82 questions correctly with a passing score of 342. GPT-3 scored highest on the “Overview of Low Bone Mass and Osteoporosis” category (72 % correct) while GPT-4 scored well above 80 % accuracy on all categories except “Imaging Technology in Bone Health” (65 % correct). Regarding subjective accuracy, GPT-3 answered 23 questions with nonsensical or totally wrong responses while GPT-4 had no responses in that category.Conclusion: If this had been an actual certification exam, GPT-4 would now have a CCD suffix to its name even after being trained using general internet knowledge. Clearly, more goes into physician training than can be captured in this exam. However, GPT algorithms may prove to be valuable physician aids in the diagnoses and monitoring of osteoporosis and other diseases.

Update on the Selection Criteria of Plastic Surgery Residents: A Survey of Program Directors and Associate Program Directors.

Plastic and reconstructive surgery (PRS) is recognized as a highly competitive specialty. Since the first assessment of resident selection criteria in 2007, PRS residency programs have adopted holistic review processes and adapted to changes such as a decline in medical schools participating in the Alpha Omega Alpha Honor Medical Society as well as the recent transition to pass/fail grading for the United States Medical Licensing Examination (USMLE) step 1 examination (Schultz et al. Plast Reconstr Surg Glob Open. 2020;8:e2892; Tadisina et al. Plast Reconstr Surg. 2017;139:330e-331e). This study was devised to evaluate current PRS residency criteria in light of these changes. An anonymous, 12-item, electronic survey was generated and distributed using Alchemer. An email was sent to 171 program directors (PDs) and associate program directors (APDs) of PRS residency programs. Survey questions were developed to collect data regarding respondent demographics and their desired criteria when assessing residency applicants. Complete responses were collected and analyzed with summary statistics and multivariate logistic regression using RStudio (version 1.3.109). In total, 44 (25.7% response rate) of the 171 PDs and APDs completed the survey. Of the 16 programs (36.4%) with a USMLE cutoff score, 7 (43.8%) reported a range of 230 to 239 and 6 (37.5%) reported a range of 240 to 249. Without a score for step 1, the majority (48.8%) of respondents believe that step 2 scores will replace step 1 scores in terms of assessment criteria, and the content of recommendation letters was selected as the criterion with the greatest increase in weight (66.7%). In addition, 27.3% of programs require a step 2 score at the time of interview. The top 3 academic criteria in order of decreasing importance were the content of recommendation letters, clinical grades, and letter writers, whereas the top 3 nonacademic criteria were subinternship performance, maturity, and interview performance. Plastic and reconstructive surgery remains a highly competitive specialty for residency applicants. Our findings suggest that Alpha Omega Alpha membership remains diminished in importance, whereas USMLE cutoff scores have increased. With recent changes in the step 1 grading system, PDs and APDs will rely more heavily on step 2 scores and the content of recommendation letters.

Effect of United States Medical Licensing Examination Score Cutoffs on Recruitment of Underrepresented Applicants in the Urology Match

To determine how the use of United States Medical Licensing Examination (USMLE) score cutoffs during the screening process of the Urology Residency Match Program may affect recruitment of applicants who are underrepresented in medicine (URM). Deidentified data from the Association of American Medical Colleges' (AAMC) Electronic Residency Application Service (ERAS) system was reviewed, representing all applicants to our institution's urology residency program from 2018 to 2022. We analyzed self-reported demographic variables including race/ethnicity, age, sex/gender, as well as USMLE Step 1 and Step 2 scores. Chi-square tests and ANOVA were used to determine the association between race/ethnicity and other sociodemographic factors and academic metrics. Applicants were stratified according to USMLE Step 1 cutoff scores and the distribution of applicants by race/ethnicity was assessed using a Gaussian nonlinear regression fit. A total of 1258 applicants submitted applications to our program during the 5-year period, including 872 males (69.3%) and 386 females (30.7%). Most applicants were White (43.5%), followed by Asian (28.3%), Hispanic/Latino (11.7%), and Black (7.0%). There was an association between race/ethnicity and USMLE scores. Median USMLE Step 1 scores for White, Asian, Hispanic/Latino, and Black applicants were 242, 242, 237, and 232, respectively (P<.001). As cutoff score increases, percentage of URM applicants decreases. The use of cutoffs based on USMLE scores disproportionately affects URM applicants. Transitioning from numeric scores to pass/fail may enhance holistic review processes and increase the representation of URM applicants offered interviews at urology residency programs.

Flipping Tradition on Its Head: A Single-Institution Study on United States Medical Licensing Examination Step Order.

Given the recent change in Step 1 grading, medical students are faced with decisions surrounding the optimal Step examination order and timing. There is a current lack of guidance outside commonly used United States Medical Licensing Examination study blogs. The aim of this study was to characterize current practices in Step examination order and understand the factors influencing student examination preference. To gather data, we used a 24-item survey and electronically distributed it through three separate email requests to medical students. Participants included medical students (MS1-4) attending a single institution, interested in a surgical residency. In total, 56 students responded to the survey (response rate of 20%). Most students (63.6%) opted to take Step 2 before Step 1. Justifications included the perceived importance of Step 2 for residency applications (33.3%), the change to pass/fail grading (27.0%), the relevance of Step 2 after rotations (27.0%), and a desire to obtain a Step 2 score earlier (7.9%). Step examination order did not lead to differences in Step 2 score (P = 0.459), time used to prepare for Step 1 (P = 0.396), or time used to prepare for Step 2 (P = 0.078). Due to the perceived importance of Step 2, most medical students at our institution who are interested in pursuing a surgical residency are reversing the traditional Step examination order. As this may represent a national trend, additional work needs to be done to provide guidance to medical students and identify the optimal step examination testing strategy.

Implementation of a dermatology skin of color educational module for medical students.

Research in dermatology education highlights the lack of skin of color (SOC) instruction for medical students, leading to concerning healthcare outcomes. Because of the already limited opportunity for students to have dedicated teaching in pathophysiology, management, and treatment of dermatologic diseases in medical school, we developed an educational module that addresses these gaps. We created a one-hour virtual lecture for medical students focused on common skin diseases tested on the United States Medical Licensing Examination with visual images across all skin types. A questionnaire was administered before and after the educational module to assess outcomes comparing disease identification in lighter (Fitzpatrick scale I-III) versus darker (Fitzpatrick scale IV-VI) skin tones and to determine medical school student attitudes. An analysis of 43 examination scores before, and after attending the educational module determined rosacea, psoriasis, and basal cell carcinoma to be conditions in SOC patients that demonstrated the most significant improvement (47.3%, 54.9%, and 30.8%, respectively). Our results also highlighted worse performance outcomes for diseases in SOC in the pre-examination questionnaire. Thus, our study indicates that a concise education module focused on disease presentations inclusive of all skin types may efficiently increase students' ability to identify diseases commonly misdiagnosed in the clinical setting.

Performance of Generative Pretrained Transformer on the National Medical Licensing Examination in Japan.

The remarkable performance of ChatGPT, launched in November 2022, has significantly impacted the field of natural language processing, inspiring the application of large language models as supportive tools in clinical practice and research worldwide. Although GPT-3.5 recently scored high on the United States Medical Licensing Examination, its performance on medical licensing examinations of other nations, especially non-English speaking nations, has not been sufficiently evaluated. This study assessed GPT's performance on the National Medical Licensing Examination (NMLE) in Japan and compared it with the actual minimal passing rate for this exam. In particular, the performances of both the GPT-3.5 and GPT-4 models were considered for the comparative analysis. We initially used the GPT models and several prompts for 290 questions without image data from the 116th NMLE (held in February 2022 in Japan) to maximize the performance for delivering correct answers and explanations of the questions. Thereafter, we tested the performance of the best GPT model (GPT-4) with optimized prompts on a dataset of 262 questions without images from the latest 117th NMLE (held in February 2023). The best model with the optimized prompts scored 82.7% for the essential questions and 77.2% for the basic and clinical questions, both of which sufficed the minimum passing scoring rates of 80.0% and 74.6%, respectively. After an exploratory analysis of 56 incorrect answers from the model, we identified the three major factors contributing to the generation of the incorrect answers-insufficient medical knowledge, information on Japan-specific medical system and guidelines, and mathematical errors. In conclusion, GPT-4 with our optimized prompts achieved a minimum passing scoring rate in the latest 117th NMLE in Japan. Beyond its original design of answering examination questions for humans, these artificial intelligence (AI) models can serve as one of the best "sidekicks" for solving problems and addressing the unmet needs in the medical and healthcare fields.

Pure Wisdom or Potemkin Villages? A Comparison of ChatGPT 3.5 and ChatGPT 4 on USMLE Step 3 Style Questions: Quantitative Analysis.

The United States Medical Licensing Examination (USMLE) has been critical in medical education since 1992, testing various aspects of a medical student's knowledge and skills through different steps, based on their training level. Artificial intelligence (AI) tools, including chatbots like ChatGPT, are emerging technologies with potential applications in medicine. However, comprehensive studies analyzing ChatGPT's performance on USMLE Step 3 in large-scale scenarios and comparing different versions of ChatGPT are limited. This paper aimed to analyze ChatGPT's performance on USMLE Step 3 practice test questions to better elucidate the strengths and weaknesses of AI use in medical education and deduce evidence-based strategies to counteract AI cheating. A total of 2069 USMLE Step 3 practice questions were extracted from the AMBOSS study platform. After including 229 image-based questions, a total of 1840 text-based questions were further categorized and entered into ChatGPT 3.5, while a subset of 229 questions were entered into ChatGPT 4. Responses were recorded, and the accuracy of ChatGPT answers as well as its performance in different test question categories and for different difficulty levels were compared between both versions. Overall, ChatGPT 4 demonstrated a statistically significant superior performance compared to ChatGPT 3.5, achieving an accuracy of 84.7% (194/229) and 56.9% (1047/1840), respectively. A noteworthy correlation was observed between the length of test questions and the performance of ChatGPT 3.5 (ρ=-0.069; P=.003), which was absent in ChatGPT 4 (P=.87). Additionally, the difficulty of test questions, as categorized by AMBOSS hammer ratings, showed a statistically significant correlation with performance for both ChatGPT versions, with ρ=-0.289 for ChatGPT 3.5 and ρ=-0.344 for ChatGPT 4. ChatGPT 4 surpassed ChatGPT 3.5 in all levels of test question difficulty, except for the 2 highest difficulty tiers (4 and 5 hammers), where statistical significance was not reached. In this study, ChatGPT 4 demonstrated remarkable proficiency in taking the USMLE Step 3, with an accuracy rate of 84.7% (194/229), outshining ChatGPT 3.5 with an accuracy rate of 56.9% (1047/1840). Although ChatGPT 4 performed exceptionally, it encountered difficulties in questions requiring the application of theoretical concepts, particularly in cardiology and neurology. These insights are pivotal for the development of examination strategies that are resilient to AI and underline the promising role of AI in the realm of medical education and diagnostics.