An Orchestration Platform that Puts Radiologists in the Driver’s Seat of AI Innovation: a Methodological Approach

Cohen, Raphael Y.; Sodickson, Aaron

doi:10.1007/s10278-022-00649-0

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…AI can standardize every scan and reduce any artifacts. It can help neuroradiologists by identifying abnormalities that are the most time-sensitive decision and annotating multiple images and other related clinical information at the same time ( 42 ).…”

Section: Collaboration Of Ai and Neuroradiologistsmentioning

confidence: 99%

Artificial intelligence in neuroradiology: a scoping review of some ethical challenges

Khosravi

Schweitzer

2023

Front. Radiol.

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Artificial intelligence (AI) has great potential to increase accuracy and efficiency in many aspects of neuroradiology. It provides substantial opportunities for insights into brain pathophysiology, developing models to determine treatment decisions, and improving current prognostication as well as diagnostic algorithms. Concurrently, the autonomous use of AI models introduces ethical challenges regarding the scope of informed consent, risks associated with data privacy and protection, potential database biases, as well as responsibility and liability that might potentially arise. In this manuscript, we will first provide a brief overview of AI methods used in neuroradiology and segue into key methodological and ethical challenges. Specifically, we discuss the ethical principles affected by AI approaches to human neuroscience and provisions that might be imposed in this domain to ensure that the benefits of AI frameworks remain in alignment with ethics in research and healthcare in the future.

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Section: Collaboration Of Ai and Neuroradiologistsmentioning

confidence: 99%

Artificial intelligence in neuroradiology: a scoping review of some ethical challenges

Khosravi

Schweitzer

2023

Front. Radiol.

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“…39 The orchestration process can be used for continuous performance improvement of imaging AI in radiology. 40…”

Section: Machine Learning Orchestration and Automation For Efficiencymentioning

confidence: 99%

Emerging Value-Based Radiology in the Era of Artificial Intelligence

Mun

Wong

et al. 2023

MRAJ

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Radiology has a long history of adopting state-of-the-art digital technology to provide better diagnostic services and facilitate advances in image-based therapeutics throughout the healthcare system. The radiology community has been developing diagnostic artificial intelligence (AI) tools over the past 20 years, long before AI became fashionable in the public press. Currently, there are approximately four hundred Food and Drug Administration approved imaging AI products. However, the clinical adoption of these products in radiology has been relatively dismal, indicating that the current technology-push model needs to evolve into the demand-pull model. We will review the current state of AI use in radiology from the perspective of clinical adoption and explore the ways in which AI products will become an ensemble of critically important tools to help radiology transition from volume-based service to value-based healthcare. This transition will create new demands for AI technologies. We contrast the current “technology-push” model with a “demand-pull” model that will aligns technology with user priorities. We summarize the lessons learned from AI experience over the past twenty years, mainly working with computer-aided detection for breast cancers and lung cancers. The radiology community calls for AI tools that can do more than detection with increasing attention toward higher workflow efficiency and higher productivity of radiologists. Major radiological societies of North America and Europe promulgated the emerging concept of value-based radiology service, an integral part of overall value-based healthcare. The transition to value-based radiology will happen and that higher value will come from the effective use of AI throughout the radiology workflow. The value-based radiology will need to work with a full range of machine learning tools, including supervised, unsupervised, and reinforcement learning, as well as natural language processing and large language models (e.g., chatbots). The engineering community is rapidly developing many concepts and sophisticated software tools for data orchestration, AI orchestration, and automation orchestration. Current radiology operation has been supported by PACS, a monolithic IT infrastructure of past generations. This system will need to migrate to an intelligence management system to support the new workflow needed for high value radiology.

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A Methodology for a Scalable, Collaborative, and Resource-Efficient Platform, MERLIN, to Facilitate Healthcare AI Research

Cohen

Kovacheva

2023

IEEE J. Biomed. Health Inform.

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An Orchestration Platform that Puts Radiologists in the Driver’s Seat of AI Innovation: a Methodological Approach

Cited by 7 publications

References 11 publications

Artificial intelligence in neuroradiology: a scoping review of some ethical challenges

Artificial intelligence in neuroradiology: a scoping review of some ethical challenges

Emerging Value-Based Radiology in the Era of Artificial Intelligence

A Methodology for a Scalable, Collaborative, and Resource-Efficient Platform, MERLIN, to Facilitate Healthcare AI Research

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