No abstract

Background Game-based approaches, or gamification, are popular learning strategies in medical education for health care providers and patients alike. Gamification has taken the form of serious educational games and simulations to enable learners to rehearse skills and knowledge in a safe environment. Dermatology learners in particular may benefit from gamification methods, given the visual and procedural nature of the field. Objective This narrative review surveys current applications of gamification within general medical training, in the education of dermatology students, and in dermatology patient outreach. Methods A literature search was performed using PubMed, Google Scholar, and ResearchGate to access and review relevant medical education- and dermatology-related gamification studies published in peer-reviewed journals. Two independent researchers with education and experience in dermatology screened publications to select studies featuring a diversity of gamification approaches and study subjects for in-depth examination. Results A total of 6 general medical education–related and 7 dermatology-specific gamification studies were selected. Gamification generally increased motivation and engagement, improved reinforcement of learning objectives, and contributed to more enjoyable and positive educational experiences compared to traditional modes of instruction. Enhancing examination scores, building confidence, and developing stronger team dynamics were additional benefits for medical trainees. Despite the abundance of gamification studies in general medical education, comparatively few instances were specific to dermatology learning, although large organizations such as the American Academy of Dermatology have begun to implement these strategies nationally. Gamification may also a provide promising alternative means of diversifying patient education and outreach methods, especially for self-identification of malignant melanoma. Conclusions Serious games and simulations in general medical education have successfully increased learner motivation, enjoyment, and performance. In limited preliminary studies, gamified approaches to dermatology-specific medical education enhanced diagnostic accuracy and interest in the field. Game-based interventions in patient-focused educational pilot studies surrounding melanoma detection demonstrated similar efficacy and knowledge benefits. However, small study participant numbers and large variability in outcome measures may indicate decreased generalizability of findings regarding the current impact of gamification approaches, and further investigation in this area is warranted. Additionally, some relevant studies may have been omitted by the simplified literature search strategy of this narrative review. This could be expanded upon in a secondary systematic review of gamified educational platforms.

UNSTRUCTURED This study underscores the persistent underrepresentation of women in academic dermatology leadership positions by examining the gender composition of editorial boards across top dermatology journals, emphasizing the urgent need for proactive strategies to promote diversity, equity, and inclusion.

Background The early diagnosis of Alzheimer’s disease (AD) and other types of dementia is essential in clinical practice. Up to 50% of patients with any form of dementia may remain undiagnosed during their lifetime and the diagnosis of AD may often be inaccurate. In assessing patients, accurate diagnosis based on only clinical data and structural (MRI or CT) imaging is suboptimal in a subset of patients with atypical presentation. Although amyloid PET imaging and CSF biomarkers can provide incremental benefits, numerous obstacles preclude their wider use in clinical practice. In this work, a parametric study was performed to create a machine learning (ML) model to accurately diagnose hard‐to‐detect cases of AD, to mitigate the need for more expensive or invasive testing such as amyloid imaging or spinal tap. Method Data from 173 participants (MCI and mild dementia), including 105 patients (60%) with AD and 68 (40%) Non‐AD patients, were included from our memory clinic. The dataset for each participant included pertinent history, AD risk factors, scores on a neuropsychological testing battery, functional status, and MRI volumetric studies. Participants were divided into two groups: Group I (easy‐to‐detect) with a high probability of AD diagnosis based on clinical data (subjective and objective amnestic presentation) and volumetric studies (hippocampal and medial temporal lobe atrophy). Group II (hard‐to‐detect), who did not fit into the first group and the diagnosis was confirmed with either amyloid imaging or CSF analysis. The distribution of diagnosis by group: 1‐ Easy to detect (43 AD, 29 Non‐AD),2‐ Hard to detect (62 AD, 39 Non‐AD), a total of three experiments with different combinations of groups were conducted, 1‐ All Groups (Exp.1.), 2‐ Hard to detect (Exp.2.), 3‐ Easy to detect (Exp.3.). In all experiments, Leave‐One‐Out cross‐validation technique was performed. Result A total number of 132 features utilized, by employing feature selection approach, 13 features were selected. Our model attains the highest accuracy of 87.27% on classification of Exp.1., 83.28% on Exp.2. and 95.80% on Exp.3. Conclusion We investigated discrimination of AD from Non‐AD on hard‐to‐detect cases using ML. The proposed method obtains competitive performance and achieves improved accuracy results.

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