Natural language processing in radiology: Clinical applications and future directions.

Natural language processing (NLP) is a wide range of techniques that allows computers to interact with human text. Applications of NLP in everyday life include language translation aids, chat bots, and text prediction. It has been increasingly utilized in the medical field with increased reliance on electronic health records. As findings in radiology are primarily communicated via text, the field is particularly suited to benefit from NLP based applications. Furthermore, rapidly increasing imaging volume will continue to increase burden on clinicians, emphasizing the need for improvements in workflow. In this article, we highlight the numerous non-clinical, provider focused, and patient focused applications of NLP in radiology. We also comment on challenges associated with development and incorporation of NLP based applications in radiology as well as potential future directions.

Comprehensiveness of Breast Radiology Fellowship Online Content.

OBJECTIVE: The content of websites for fellowship programs is an important source of information for residents applying to breast imaging fellowship programs (BIFPs). The purpose of this study is to evaluate the comprehensiveness of online content of BIFPs. METHODS: A list of BIFPs was obtained from the Society of Breast Imaging website. Each program's website was evaluated for the presence of 19 training-relevant content variables. Impact of program characteristics on comprehensiveness scores was determined. For statistical analysis, Kruskal-Wallis tests were used to assess differences in comprehensiveness scores based upon region, and two-tailed t-tests were used to compare based upon program size. RESULTS: A total of 79 BIFP websites were analyzed. The mean comprehensiveness score of BIFP websites based on meeting the 19 criteria was 44.1% (8.4 ± 2.7/19). Program coordinator contact information (72/79, 91.1%), application requirements (71/79, 89.9%), and faculty information (56/79, 70.9%) appeared on >70% of websites. The majority of fellowships had a dedicated webpage for their program (71/79, 89.9%). Information regarding 12 of the 19 criteria appeared on fewer than 50% of websites. Program region (P = 0.32) and size (P = 0.16) were not associated with any differences in mean comprehensiveness score. Additionally, there was no significant difference in scores associated with filling all available positions for the 2020 match cycle (P = 0.77). CONCLUSION: There is a paucity of information commonly sought out by applicants on the websites of most BIFPs. Both programs and applicants may mutually benefit from increasing comprehensive online content.