Integrating AI into radiology workflow: levels of research, production, and feedback maturity

Dikici, Engin; Bigelow, Matthew T.; Prevedello, Luciano M.; White, Richard D.; Erdal, Barbaros S.

doi:10.1117/1.jmi.7.1.016502

Cited by 62 publications

(58 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DICOM-facilitated interoperability between AI actors and other devices in a medical imaging network is likely to facilitate workflow efficiencies. In addition to the transmission of images and results, interoperability is likely to play a key role in the continual adaptive learning of machine learning algorithms ( 39 ). Interoperability will also allow health-care organizations to use “best-of-breed” AI actors rather than being locked into an existing vendor's product.…”

Section: Discussionmentioning

confidence: 99%

The Role of DICOM in Artificial Intelligence for Skin Disease

et al. 2021

View full text Add to dashboard Cite

There is optimism that artificial intelligence (AI) will result in positive clinical outcomes, which is driving research and investment in the use of AI for skin disease. At present, AI for skin disease is embedded in research and development and not practiced widely in clinical dermatology. Clinical dermatology is also undergoing a technological transformation in terms of the development and adoption of standards that optimizes the quality use of imaging. Digital Imaging and Communications in Medicine (DICOM) is the international standard for medical imaging. DICOM is a continually evolving standard. There is considerable effort being invested in developing dermatology-specific extensions to the DICOM standard. The ability to encode relevant metadata and afford interoperability with the digital health ecosystem (e.g., image repositories, electronic medical records) has driven the initial impetus in the adoption of DICOM for dermatology. DICOM has a dedicated working group whose role is to develop a mechanism to support AI workflows and encode AI artifacts. DICOM can improve AI workflows by encoding derived objects (e.g., secondary images, visual explainability maps, AI algorithm output) and the efficient curation of multi-institutional datasets for machine learning training, testing, and validation. This can be achieved using DICOM mechanisms such as standardized image formats and metadata, metadata-based image retrieval, and de-identification protocols. DICOM can address several important technological and workflow challenges for the implementation of AI. However, many other technological, ethical, regulatory, medicolegal, and workforce barriers will need to be addressed before DICOM and AI can be used effectively in dermatology.

show abstract

Section: Discussionmentioning

confidence: 99%

The Role of DICOM in Artificial Intelligence for Skin Disease

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Additionally, AI results may be available to clinicians using an enterprise viewer. Adapted from Dikici et al 68 from multiple vendors based on the greatest needs of their institution, appealing for many institutions. 63 As radiology practices consider implementing AI, aspects such as whether their IT teams have the time and resources to commit to system integration, whether the product ''plugs in'' to an existing product or whether a complete install/integration project is required, and vendor compatibility or the availability of vendor-neutral solutions, will need to be considered.…”

Section: What Should Be Considered When Selecting Ai Products For Radiograph Analysis and Integrating Them Into Existing It Systems?mentioning

confidence: 99%

“…Additionally, AI results may be available to clinicians using an enterprise viewer. Adapted from Dikici et al68…”

mentioning

confidence: 99%

Artificial Intelligence Solutions for Analysis of X-ray Images

et al. 2020

View full text Add to dashboard Cite

Artificial intelligence (AI) presents a key opportunity for radiologists to improve quality of care and enhance the value of radiology in patient care and population health. The potential opportunity of AI to aid in triage and interpretation of conventional radiographs (X-ray images) is particularly significant, as radiographs are the most common imaging examinations performed in most radiology departments. Substantial progress has been made in the past few years in the development of AI algorithms for analysis of chest and musculoskeletal (MSK) radiographs, with deep learning now the dominant approach for image analysis. Large public and proprietary image data sets have been compiled and have aided the development of AI algorithms for analysis of radiographs, many of which demonstrate accuracy equivalent to radiologists for specific, focused tasks. This article describes (1) the basis for the development of AI solutions for radiograph analysis, (2) current AI solutions to aid in the triage and interpretation of chest radiographs and MSK radiographs, (3) opportunities for AI to aid in noninterpretive tasks related to radiographs, and (4) considerations for radiology practices selecting AI solutions for radiograph analysis and integrating them into existing IT systems. Although comprehensive AI solutions across modalities have yet to be developed, institutions can begin to select and integrate focused solutions which increase efficiency, increase quality and patient safety, and add value for their patients.

show abstract

“…The last important decision makers are the end users, which in most cases are radiologists, but other clinicians, including cardiologists and neurologists, can also drive adoption. To many radiologists, nonimage interpretive workflow disruptors have shown to decrease radiologist satisfaction, adversely affect radiologist workload, and decrease self-perceived quality of image interpretation (21)(22)(23). In the current landscape of high volumes and monitored turnaround times, any decrease was trained with limited data (19,20).…”

Section: Cliniciansmentioning

confidence: 99%