October 2022
·
5 Reads
Academic Medicine
This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.
October 2022
·
5 Reads
Academic Medicine
August 2021
·
331 Reads
·
178 Citations
Academic Medicine
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. Eight 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 was 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 a 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.
June 2021
·
31 Reads
·
1 Citation
BMJ Case Reports
An 85-year-old man with a known history of abdominal aortic aneurysm (AAA) presented to a vascular surgery clinic with a severely swollen, tender and erythematous left leg. An urgent CT angiogram demonstrated a left-sided, proximal deep vein thrombosis, and a permanent, Bird’s Nest inferior vena cava (IVC) filter (Cook, Inc., Bloomington, Ind.) penetrating his AAA. The patient was treated with a course of apixaban 5 mg two times per day and the decision was made to closely observe his IVC filter and AAA, given his numerous comorbidities and age. This case highlights the unique considerations associated with an approach to permanent IVC filter complications among patients with AAAs.
... Despite these advancements, the integration of AI into medical curricula remains insufficient, with most programs failing to address the specific competencies required for students to navigate AI-driven clinical environments effectively. This has created a critical gap in medical education, leaving future healthcare professionals underprepared to utilize AI technologies in practice and to address their associated ethical and operational challenges [16]. In several academic circles, it is suggested that rather than training specific AI competencies, the focus should be on strengthening transversal skills, including autonomous learning, to facilitate the gradual incorporation of emerging technologies and other evolving components [17,18]. ...
August 2021
Academic Medicine
... Many late-onset complications of IVC filters are uncommon and are reported in individual case reports [3,[6][7][8][9][10][11][12][13][14][15][16]. These complications include filter migration, erosion, embolization, and wire fracture [1,17]. ...
June 2021
BMJ Case Reports