Association between multiple sclerosis and epilepsy: A systematic review and meta-analysis.

BACKGROUND: Seizures in people with multiple sclerosis (MS) are reported; however, the risk of epilepsy in adults with MS remains poorly defined. METHODS: We performed a systematic review and meta-analysis to evaluate the incidence and prevalence of epilepsy in adults (≥ 18 years) with MS compared to those without. We searched MEDLINE and EMBASE from inception to July 1, 2022 to include observational studies that reported the prevalence or incidence of epilepsy in adults with MS and a comparator group, consisting of adults without MS or the general population. We used the Newcastle Ottawa Scale to evaluate quality of the included studies. We performed random-effects meta-analyses to determine the prevalence and incidence of epilepsy in adults with MS compared to the non-MS group. RESULTS: We identified 17 studies consisting of 192,850 adults with MS, across nine countries. Most studies were of moderate quality as they did not specify the MS type or type of seizures. Compared to a comparison group, both the prevalence (pooled OR 2.04; 95% confidence interval 1.59-2.63, I2 = 95.4, n = 12) and the incidence of epilepsy (pooled RR 3.34; 3.17-3.52, I2 = 4.6%, n = 6) was higher in people with MS. Heterogeneity in estimates was not explained by additional analyses. CONCLUSIONS: MS is an independent risk factor for both incident and prevalent epilepsy, suggesting variation in grey matter involvement over the disease course. Longitudinal studies are required to help identify patient and disease characteristics associated with epilepsy.

Artificial Intelligence in Undergraduate Medical Education: A Scoping Review.

PURPOSE: Artificial intelligence (AI) is a rapidly growing phenomenon poised to instigate large-scale changes in medicine. However, medical education has not kept pace with the rapid advancements of AI. Despite several calls to action, the adoption of teaching on AI in undergraduate medical education (UME) has been limited. This scoping review aims to identify gaps and key themes in the peer-reviewed literature on AI training in UME. METHOD: The scoping review was informed by Arksey and O'Malley's methodology. Seven electronic databases including MEDLINE and EMBASE were searched for articles discussing the inclusion of AI in UME between January 2000 and July 2020. A total of 4,299 articles were independently screened by 3 co-investigators and 22 full-text articles were included. Data were extracted using a standardized checklist. Themes were identified using iterative thematic analysis. RESULTS: The literature addressed: (1) a need for an AI curriculum in UME, (2) recommendations for AI curricular content including machine learning literacy and AI ethics, (3) suggestions for curriculum delivery, (4) an emphasis on cultivating "uniquely human skills" such as empathy in response to AI-driven changes, and (5) challenges with introducing an AI curriculum in UME. However, there was considerable heterogeneity and poor consensus across studies regarding AI curricular content and delivery. CONCLUSIONS: Despite the large volume of literature, there is little consensus on what and how to teach AI in UME. Further research is needed to address these discrepancies and create a standardized framework of competencies that can facilitate greater adoption and implementation of a standardized AI curriculum in UME.