Reevaluation of the US Pathologist Workforce Size

Robboy, Stanley J.; Gross, David J.; Park, Jason; Kittrie, Elizabeth; Crawford, James M.; Johnson, Rebecca L.; Cohen, Michael B.; Karcher, Donald S.; Hoffman, Robert D.; Smith, Anthony T.; Black-Schaffer, W. Stephen

doi:10.1001/jamanetworkopen.2020.10648

Cited by 34 publications

(25 citation statements)

References 11 publications

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“… 1 , 2 , 3 , 4 , 5 , 6 There is on-going concern for maintaining an adequate number of practicing pathologists to meet the needs of the US healthcare system, and how best to plan for an optimal workforce. 7 , 8 , 9 …”

Section: Introductionmentioning

confidence: 99%

Factors influencing US osteopathic medical students to choose pathology as a specialty

et al. 2022

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Section: Introductionmentioning

confidence: 99%

Factors influencing US osteopathic medical students to choose pathology as a specialty

et al. 2022

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“…Moreover, our study was specifically designed to evaluate the rate of cases with inconclusive diagnoses and not all of them would be sent out for extradepartmental consultation in a real-world scenario. Most institutions (95%) who participated in the CAP survey were from the USA [ 21 ], which has the largest number of pathologists (over 20,000) in the world [ 22 ], providing opportunities for easier access to subspecialty experts within the same institution. In contrast, our study results would be more applicable to mid-range laboratories of countries with relatively lower pathologist workforce.…”

Section: Discussionmentioning

confidence: 99%

Substantial improvement of histopathological diagnosis by whole-slide image-based remote consultation

et al. 2022

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Consultation by subspecialty experts is the most common mode of rendering diagnosis in challenging cases in pathological practice. Our study aimed to highlight the diagnostic benefits of whole-slide image (WSI)-based remote consultation. We obtained diagnostically challenging cases from two institutions from the years 2010 and 2013, with histological diagnoses that contained keywords “probable,” “suggestive,” “suspicious,” “inconclusive,” and “uncertain.” A total of 270 cases were selected for remote consultation using WSIs scanned at 40 × . The consultation process consisted of three rounds: the first and second rounds each with 12 subspecialty experts and the third round with six multi-expertise senior pathologists. The first consultation yielded 44% concordance, and a change in diagnosis occurred in 56% of cases. The most frequent change was from inconclusive to definite diagnosis (30%), followed by minor discordance (14%), and major discordance (12%). Out of the 70 cases which reached the second round, 31 cases showed discrepancy between the two consultants. For these 31 cases, a consensus diagnosis was provided by six multi-expertise senior pathologists. Combining all WSI-based consultation rounds, the original inconclusive diagnosis was changed in 140 (52%) out of 266 cases. Among these cases, 80 cases (30%) upgraded the inconclusive diagnosis to a definite diagnosis, and 60 cases (22%) changed the diagnosis with major or minor discordance, accounting for 28 cases (10%) and 32 cases (12%), respectively. We observed significant improvement in the pathological diagnosis of difficult cases by remote consultation using WSIs, which can further assist in patient healthcare. A post-study survey highlighted various benefits of WSI-based consults. Supplementary Information The online version contains supplementary material available at 10.1007/s00428-022-03327-2.

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“…Thus, annotation of pathological tissues costs a lot of time and money. However, there is a chronic shortage of pathologists [41,42], making it almost impossible to obtain a large number of annotations in reality, i.e. in clinical settings.…”

Section: Discussionmentioning

confidence: 99%

MIXTURE of human expertise and deep learning—Developing an explainable model for predicting pathological diagnosis and survival in patients with interstitial lung disease

Uegami

Bychkov

Ozasa

et al. 2021

Preprint

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Interstitial pneumonia is a heterogeneous disease with a progressive course and poor prognosis, at times even worse than those in the main cancer types. Histopathological examination is crucial for its diagnosis and estimation of prognosis. However, the evaluation strongly depends on the experience of pathologists, and the reproducibility of diagnosis is low. Herein, we propose MIXTURE (huMan-In-the-loop eXplainable artificial intelligence Through the Use of REcurrent training), a method to develop deep learning models for extracting pathologically significant findings based on an expert pathologist's perspective with a small annotation effort. The procedure of MIXTURE consists of three steps as follows. First, we created feature extractors for tiles from whole slide images using self-supervised learning. The similar looking tiles were clustered based on the output features and then pathologists integrated the pathologically synonymous clusters. Using the integrated clusters as labeled data, deep learning models to classify the tiles into pathological findings were created by transfer-learning the feature extractors. We developed three models for different magnifications. Using these extracted findings, our model was able to predict the diagnosis of usual interstitial pneumonia, a finding suggestive of progressive disease, with high accuracy (AUC 0.90). This high accuracy could not be achieved without the integration of findings by pathologists. The patients predicted as UIP had significantly poorer prognosis (five-year overall survival [OS]: 55.4% than those predicted as non-UIP (OS: 95.2%). The Cox proportional hazards model for each microscopic finding and prognosis pointed out dense fibrosis, fibroblastic foci, elastosis, and lymphocyte aggregation as independent risk factors. We suggest that MIXTURE may serve as a model approach to different diseases evaluated by medical imaging, including pathology and radiology, and be the prototype for artificial intelligence that can collaborate with humans.

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Reevaluation of the US Pathologist Workforce Size

Abstract: IMPORTANCE There is currently no national organization that publishes its data that serves as the authoritative source of the pathologist workforce in the US. Accurate physician numbers are needed to plan for future health care service requirements.

Cited by 34 publications

References 11 publications

Factors influencing US osteopathic medical students to choose pathology as a specialty

Factors influencing US osteopathic medical students to choose pathology as a specialty

Substantial improvement of histopathological diagnosis by whole-slide image-based remote consultation

MIXTURE of human expertise and deep learning—Developing an explainable model for predicting pathological diagnosis and survival in patients with interstitial lung disease

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