Preparing Radiologists to Lead in the Era of Artificial Intelligence:                     Designing and Implementing a Focused Data Science Pathway for Senior Radiology                     Residents

Wiggins, Walter F.; Caton, M Travis; Magudia, Kirti; Glomski, Sha-har A.; George, Elizabeth; Rosenthal, Michael H.; Gaviola, Glenn C.; Andriole, Katherine P.

doi:10.1148/ryai.2020200057

Cited by 37 publications

(14 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Wiggings et al [ 26 ] designed and implemented a focused data science pathway for senior radiology residents. In this model, three fourth-year residents with varying technical background were involved in a data science pathway aimed to address all stages of clinical ML model development (fundamentals, data curation, model development and clinical integration) with proper mentorship.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

The current state of knowledge on imaging informatics: a survey among Spanish radiologists

Eiroa¹,

Antolín²,

Ascanio

et al. 2022

Insights Imaging

View full text Add to dashboard Cite

Background There is growing concern about the impact of artificial intelligence (AI) on radiology and the future of the profession. The aim of this study is to evaluate general knowledge and concerns about trends on imaging informatics among radiologists working in Spain (residents and attending physicians). For this purpose, an online survey among radiologists working in Spain was conducted with questions related to: knowledge about terminology and technologies, need for a regulated academic training on AI and concerns about the implications of the use of these technologies. Results A total of 223 radiologists answered the survey, of whom 76.7% were attending physicians and 23.3% residents. General terms such as AI and algorithm had been heard of or read in at least 75.8% and 57.4% of the cases, respectively, while more specific terms were scarcely known. All the respondents consider that they should pursue academic training in medical informatics and new technologies, and 92.9% of them reckon this preparation should be incorporated in the training program of the specialty. Patient safety was found to be the main concern for 54.2% of the respondents. Job loss was not seen as a peril by 45.7% of the participants. Conclusions Although there is a lack of knowledge about AI among Spanish radiologists, there is a will to explore such topics and a general belief that radiologists should be trained in these matters. Based on the results, a consensus is needed to change the current training curriculum to better prepare future radiologists.

show abstract

Section: Discussionmentioning

confidence: 99%

“…The experience gained in this pilot project would help improve the training experience in consecutive years for a long-term success of this curriculum. At the end of the training, participants showed a desire for a more formal didactic curriculum [ 26 ].…”

Section: Discussionmentioning

confidence: 99%

The current state of knowledge on imaging informatics: a survey among Spanish radiologists

Eiroa¹,

Antolín²,

Ascanio

et al. 2022

Insights Imaging

View full text Add to dashboard Cite

show abstract

“…Yet, they need to learn enough about the development of AI applications to be able to bring their valuable medical experience into the development process. Radiologists should know the basic principles, limitations, risks, and pitfalls, and the techniques and technologies used for developing these applications and learn how to evaluate and monitor the algorithms [11] 3 .…”

Section: Discussionmentioning

confidence: 99%

“…These consultations happened in an informal setting and offered background information about the roles of radiologists. Checking the results of the algorithm to be accurate and stable and radiologists can trust them [10,11] To systematically analyze data, we followed four steps. First, we integrated the data from the interviews and the documents into a detailed case-study report for each case.…”

Section: Methodsmentioning

confidence: 99%

Radiologists in the loop: the roles of radiologists in the development of AI applications

2021

View full text Add to dashboard Cite

Objectives To examine the various roles of radiologists in different steps of developing artificial intelligence (AI) applications. Materials and methods Through the case study of eight companies active in developing AI applications for radiology, in different regions (Europe, Asia, and North America), we conducted 17 semi-structured interviews and collected data from documents. Based on systematic thematic analysis, we identified various roles of radiologists. We describe how each role happens across the companies and what factors impact how and when these roles emerge. Results We identified 9 roles that radiologists play in different steps of developing AI applications: (1) problem finder (in 4 companies); (2) problem shaper (in 3 companies); (3) problem dominator (in 1 company); (4) data researcher (in 2 companies); (5) data labeler (in 3 companies); (6) data quality controller (in 2 companies); (7) algorithm shaper (in 3 companies); (8) algorithm tester (in 6 companies); and (9) AI researcher (in 1 company). Conclusions Radiologists can play a wide range of roles in the development of AI applications. How actively they are engaged and the way they are interacting with the development teams significantly vary across the cases. Radiologists need to become proactive in engaging in the development process and embrace new roles. Key Points • Radiologists can play a wide range of roles during the development of AI applications. • Both radiologists and developers need to be open to new roles and ways of interacting during the development process. • The availability of resources, time, expertise, and trust are key factors that impact how actively radiologists play roles in the development process.

show abstract

“…AI methods in MRI have been used to support decision making in breast cancer diagnosis and prognosis for several decades [ 38 , 39 ]. As the scope and sophistication of AI in medical imaging grows, some have noted that it would be beneficial to implement specific data science curriculum for radiology trainees [ 40 , 41 ]. Education in harmonization of data or feature selection could potentially be one element of such training, but at this time, the harmonization methods described here are in development and have not yet been approved for clinical use.…”

Section: Discussionmentioning

confidence: 99%

Multi-Stage Harmonization for Robust AI across Breast MR Databases

Whitney

et al. 2021

Cancers

View full text Add to dashboard Cite

Radiomic features extracted from medical images may demonstrate a batch effect when cases come from different sources. We investigated classification performance using training and independent test sets drawn from two sources using both pre-harmonization and post-harmonization features. In this retrospective study, a database of thirty-two radiomic features, extracted from DCE-MR images of breast lesions after fuzzy c-means segmentation, was collected. There were 944 unique lesions in Database A (208 benign lesions, 736 cancers) and 1986 unique lesions in Database B (481 benign lesions, 1505 cancers). The lesions from each database were divided by year of image acquisition into training and independent test sets, separately by database and in combination. ComBat batch harmonization was conducted on the combined training set to minimize the batch effect on eligible features by database. The empirical Bayes estimates from the feature harmonization were applied to the eligible features of the combined independent test set. The training sets (A, B, and combined) were then used in training linear discriminant analysis classifiers after stepwise feature selection. The classifiers were then run on the A, B, and combined independent test sets. Classification performance was compared using pre-harmonization features to post-harmonization features, including their corresponding feature selection, evaluated using the area under the receiver operating characteristic curve (AUC) as the figure of merit. Four out of five training and independent test scenarios demonstrated statistically equivalent classification performance when compared pre- and post-harmonization. These results demonstrate that translation of machine learning techniques with batch data harmonization can potentially yield generalizable models that maintain classification performance.

show abstract

Preparing Radiologists to Lead in the Era of Artificial Intelligence: Designing and Implementing a Focused Data Science Pathway for Senior Radiology Residents

Cited by 37 publications

References 24 publications

The current state of knowledge on imaging informatics: a survey among Spanish radiologists

The current state of knowledge on imaging informatics: a survey among Spanish radiologists

Radiologists in the loop: the roles of radiologists in the development of AI applications

Multi-Stage Harmonization for Robust AI across Breast MR Databases

Contact Info

Product

Resources

About