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Objective: To develop a free, vendor-neutral software suite, the American College of Radiology (ACR) Connect, which serves as a platform for democratizing artificial intelligence (AI) for all individuals and institutions. Materials and Methods: Among its core capabilities, ACR Connect provides educational resources; tools for dataset annotation; model building and evaluation; and an interface for collaboration and federated learning across institutions without the need to move data off hospital premises. Results: The AI-LAB application within ACR Connect allows users to investigate AI models using their own local data while maintaining data security. The software enables non-technical users to participate in the evaluation and training of AI models as part of a larger, collaborative network. Discussion: Advancements in AI have transformed automated quantitative analysis for medical imaging. Despite the significant progress in research, AI is currently underutilized in current clinical workflows. The success of AI model development depends critically on the synergy between physicians who can drive clinical direction, data scientists who can design effective algorithms, and the availability of high-quality datasets. ACR Connect and AI-LAB provide a way to perform external validation as well as collaborative, distributed training. Conclusion: In order to create a collaborative AI ecosystem across clinical and technical domains, the ACR developed a platform that enables non-technical users to participate in education and model development.
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OBJECTIVE: Clinical Knowledge Authoring Tools (CKATs) are integral to the computerized Clinical Decision Support (CDS) development life cycle. CKATs enable authors to generate accurate, complete, and reliable digital knowledge artifacts in a relatively efficient and affordable manner. This scoping review aims to compare knowledge authoring tools and derive the common features of CKATs. MATERIALS AND METHODS: We performed a keyword-based literature search, followed by a snowball search, to identify peer-reviewed publications describing the development or use of CKATs. We used PubMed and Embase search engines to perform the initial search (n = 1579). After removing duplicate articles, nonrelevant manuscripts, and not peer-reviewed publication, we identified 47 eligible studies describing 33 unique CKATs. The reviewed CKATs were further assessed, and salient characteristics were extracted and grouped as common CKAT features. RESULTS: Among the identified CKATs, 55% use an open source platform, 70% provide an application programming interface for CDS system integration, and 79% provide features to validate/test the knowledge. The majority of the reviewed CKATs describe the flow of information, offer a graphical user interface for knowledge authors, and provide intellisense coding features (94%, 97%, and 97%, respectively). The composed list of criteria for CKAT included topics such as simulating the clinical setting, validating the knowledge, standardized clinical models and vocabulary, and domain independence. None of the reviewed CKATs met all common criteria. CONCLUSION: Our scoping review highlights the key specifications for a CKAT. The CKAT specification proposed in this review can guide CDS authors in developing more targeted CKATs.
ABSTRACT
OBJECTIVES: The aim of this study was to compare how often fine-needle aspiration (FNA) would be recommended for nodules in unselected, low-risk adult patients referred for sonographic evaluation of thyroid nodules by ACR Thyroid Imaging Reporting and Data System (TI-RADS), the American Thyroid Association guidelines (ATA), Korean Thyroid Imaging Reporting and Data System (K-TIRADS), European Thyroid Imaging Reporting and Data System (EU-TIRADS), and Artificial Intelligence Thyroid Imaging Reporting and Data System (AI-TIRADS). METHODS: Seven practices prospectively submitted thyroid ultrasound reports on adult patients to the ACR Thyroid Imaging Research Registry between October 2018 and March 2020. Data were collected about the sonographic features of each nodule using a structured reporting template with fields for the five ACR TI-RADS ultrasound categories plus maximum nodule size. The nodules were also retrospectively categorized according to criteria from ACR TI-RADS, the ATA, K-TIRADS, EU-TIRADS, and AI-TIRADS to compare FNA recommendation rates. RESULTS: For 27,933 nodules in 12,208 patients, ACR TI-RADS recommended FNA for 8,128 nodules (29.1%, 95% confidence interval [CI] 0.286-0.296). The ATA guidelines, EU-TIRADS, K-TIRADS, and AI-TIRADS would have recommended FNA for 16,385 (58.7%, 95% CI 0.581-0.592), 10,854 (38.9%, 95% CI 0.383-0.394), 15,917 (57.0%, 95% CI 0.564-0.576), and 7,342 (26.3%, 95% CI 0.258-0.268) nodules, respectively. Recommendation for FNA on TR3 and TR4 nodules was lowest for ACR TI-RADS at 18% and 30%, respectively. ACR TI-RADS categorized more nodules as TR2, which does not require FNA. At the high suspicion level, the FNA rate was similar for all guidelines at 68.7% to 75.5%. CONCLUSION: ACR TI-RADS recommends 25% to 50% fewer biopsies compared with ATA, EU-TIRADS, and K-TIRADS because of differences in size thresholds and criteria for risk levels.
