Trends in the dermatology residency match from 2007 to 2018: Implications for the dermatology workforce

Jayakumar, Kishore L.; Lipoff, Jules B.

doi:10.1016/j.jaad.2018.06.071

Cited by 9 publications

(4 citation statements)

References 5 publications

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“…The relatively low median h-index may reflect an applicant's limited research experience, as medical students may be more likely to publish in lower impact journals and possess short research careers. Our study found that matched dermatology candidates hold increasing research accomplishments each year, which is consistent with trends seen in the literature [2][3][13][14]. The rising h-index of successful candidates may indicate that not only quantity but the quality of publications is increasing.…”

Section: Discussionsupporting

confidence: 88%

“…Despite the expansion in the number of dermatology residency programs, the proportion of candidates failing to match in dermatology remains substantial [1][2], and it is becoming increasingly difficult to match into a dermatology residency position [3]. Multiple quantifiable factors play a role in candidate success, such as the United States Medical Licensing Examination (USMLE) scores, research productivity, and Alpha Omega Alpha (AOA) membership [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies and national data have reported on the research achievements of successful dermatology residency applicants [2][3][13][14]. However, they utilize self-reports, include abstracts and presentations within their publication count, and do not make a distinction between article subtypes, e.g.…”

Section: Introductionmentioning

confidence: 99%

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Trends in the Academic Credentials of Matched Dermatology Residency Applicants

Ngaage

Gao

et al. 2020

Cureus

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Introduction Research can be used to enhance the competitiveness of an application and is associated with a successful match. However, current reports regarding the publication record among prospective dermatology residents may be inaccurate. We sought to accurately assess the research credentials of matched dermatology residency candidates at the time of application. Methods We performed a bibliometric analysis to identify published articles of 1152 matched dermatology candidates and calculated the h-index of each applicant at the time of application. Details on article type, first authorship, and dermatology-relatedness of articles were collected. Results The median number of publications was two and the median h-index was 0. At the time of residency application, one-quarter of matched dermatology candidates (24%, n=278) possessed no publications. Over time, the median number of publications (R 0.10, p<0.001) and h-index (R 0.07, p=0.014) of matched applicants increased. The proportion of first-authored articles, dermatology-related papers, and each article type remained constant across application cycles (p>0.0500). An additional graduate degree, completion of a research fellowship, and graduation from a non-US medical school were independently associated with greater research credentials (p<0.0500). Conclusions Each year, applicants are publishing more articles and have a greater scholarly impact than in previous application cycles. However, the verified publication volume of matched dermatology applicants is strikingly lower than the values reported in national statistics.

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Section: Discussionsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

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Trends in the Academic Credentials of Matched Dermatology Residency Applicants

Ngaage

Gao

et al. 2020

Cureus

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“…Dermatology is well positioned to leverage DL to improve patient care, with its emphasis on visual analysis. Given the shortage of dermatologists in the U.S. (Jayakumar and Lipoff, 2019) and the increasing incidence of cutaneous melanoma (National Cancer Institute, 2020), AI may play an increasingly important role in improving access to and quality of dermatological care. This review summarizes research on the automated classification and monitoring of skin lesions, discusses barriers to clinical adoption, and proposes metrics for AI model performance.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Dermatology: A Primer

Young

Xiong

Pfau

et al. 2020

Journal of Investigative Dermatology

134

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Artificial intelligence is becoming increasingly important in dermatology, with studies reporting accuracy matching or exceeding dermatologists for the diagnosis of skin lesions from clinical and dermoscopic images. However, real-world clinical validation is currently lacking. We review dermatological applications of deep learning, the leading artificial intelligence technology for image analysis, and discuss its current capabilities, potential failure modes, and challenges surrounding performance assessment and interpretability. We address the following three primary applications: (i) teledermatology, including triage for referral to dermatologists; (ii) augmenting clinical assessment during face-to-face visits; and (iii) dermatopathology. We discuss equity and ethical issues related to future clinical adoption and recommend specific standardization of metrics for reporting model performance.

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