Social Asymmetry, Artificial Intelligence and the Medical Imaging Landscape

Currie, Geoffrey; Rohren, Eric M.

doi:10.1053/j.semnuclmed.2021.11.011

Cited by 33 publications

(13 citation statements)

References 28 publications

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“…We fully agree with these studies [ 46 , 47 , 48 , 49 , 50 ]. We believe that challenges and acceptance of the integration of technology must be interconnected and go hand in hand so that expectations are not disillusioned, and we can obtain Artificial Intelligence that offers us an increasingly effective and health-oriented useful approach.…”

Section: Discussionsupporting

confidence: 92%

“…Other review studies clearly showed that the pandemic represented an important engine for the development of this field [ 47 , 48 , 49 ] and an important lesson on how to continue for the future, as highlighted in the perspective reported in [ 37 ]. Other studies considered Artificial Intelligence in Digital Radiology in terms of impact to equity [ 50 ]. There was a [ 50 ] belief that Artificial Intelligence had the strength to either widen the health inequity divide or substantially reduce it.…”

Section: Discussionmentioning

confidence: 99%

“…Other studies considered Artificial Intelligence in Digital Radiology in terms of impact to equity [ 50 ]. There was a [ 50 ] belief that Artificial Intelligence had the strength to either widen the health inequity divide or substantially reduce it. The authors [ 50 ] believed that, with a careful attention in the use: entirely realized, Artificial Intelligence integrated in the health domain could be a part of the broader strategy convergence on local, national, and global health equity.…”

Section: Discussionmentioning

confidence: 99%

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The Artificial Intelligence in Digital Radiology: Part 1: The Challenges, Acceptance and Consensus

Giansanti

Basilio

2022

Healthcare

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Artificial intelligence is having important developments in the world of digital radiology also thanks to the boost given to the research sector by the COVID-19 pandemic. In the last two years, there was an important development of studies focused on both challenges and acceptance and consensus in the field of Artificial Intelligence. The challenges and acceptance and consensus are two strategic aspects in the development and integration of technologies in the health domain. The study conducted two narrative reviews by means of two parallel points of view to take stock both on the ongoing challenges and on initiatives conducted to face the acceptance and consensus in this area. The methodology of the review was based on: (I) search of PubMed and Scopus and (II) an eligibility assessment, using parameters with 5 levels of score. The results have: (a) highlighted and categorized the important challenges in place. (b) Illustrated the different types of studies conducted through original questionnaires. The study suggests for future research based on questionnaires a better calibration and inclusion of the challenges in place together with validation and administration paths at an international level.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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The Artificial Intelligence in Digital Radiology: Part 1: The Challenges, Acceptance and Consensus

Giansanti

Basilio

2022

Healthcare

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“…Surely, the COVID-19 pandemic that people are experiencing has also represented an important push in this direction [ 37 , 38 , 39 ] and given an important lesson for the future [ 40 ]. An improvement in equity of care is also expected from the integration of AI in DR into the health domain [ 41 ]. It is quite clear that integration into the health domain involves major challenges and processes of acceptance of consensus.…”

Section: Discussionmentioning

confidence: 99%

The Regulation of Artificial Intelligence in Digital Radiology in the Scientific Literature: A Narrative Review of Reviews

Giansanti

2022

Healthcare

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Today, there is growing interest in artificial intelligence (AI) in the field of digital radiology (DR). This is also due to the push that has been applied in this sector due to the pandemic. Many studies are devoted to the challenges of integration in the health domain. One of the most important challenges is that of regulations. This study conducted a narrative review of reviews on the international approach to the regulation of AI in DR. The design of the study was based on: (I) An overview on Scopus and Pubmed (II) A qualification and eligibility process based on a standardized checklist and a scoring system. The results have highlighted an international approach to the regulation of these systems classified as “software as medical devices (SaMD)” arranged into: ethical issues, international regulatory framework, and bottlenecks of the legal issues. Several recommendations emerge from the analysis. They are all based on fundamental pillars: (a) The need to overcome a differentiated approach between countries. (b) The need for greater transparency and publicity of information both for SaMDs as a whole and for the algorithms and test patterns. (c) The need for an interdisciplinary approach that avoids bias (including demographic) in algorithms and test data. (d) The need to reduce some limits/gaps of the scientific literature production that do not cover the international approach.

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“…The principles of intelligent imaging and engineered learning have been widely reported, 1 , 2 , 3 , 4 as have the social and ethical challenges. 5 , 6 , 7 These additional layers to imaging data could reduce or worsen healthcare inequities. Consequently, careful consideration of data type and diversity is required to ensure these technologies harness clinically useful, bias‐free radiomic data.…”

Section: Introductionmentioning

confidence: 99%

The transformational potential of molecular radiomics

Currie

Hawk

Rohren

2022

J of Medical Radiation Sci

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Conventional radiomics in nuclear medicine involve hand‐crafted and computer‐assisted regions of interest. Recent developments in artificial intelligence (AI) have seen the emergence of AI‐augmented segmentation and extraction of lower order traditional radiomic features. Deep learning (DL) affords the opportunity to extract abstract radiomic features directly from input tensors (images) without the need for segmentation. These fourth‐order, high dimensional radiomics produce deep radiomics and are well suited to the data density associated with the molecular nature of hybrid imaging. Molecular radiomics and deep molecular radiomics provide insights beyond images and quantitation typical of semantic reporting. While the application of molecular radiomics using hand‐crafted and computer‐generated features is integrated into decision‐making in nuclear medicine, the acceptance of deep molecular radiomics is less universal. This manuscript aims to provide an understanding of the language and principles associated with radiomics and deep radiomics in nuclear medicine.

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Social Asymmetry, Artificial Intelligence and the Medical Imaging Landscape

Cited by 33 publications

References 28 publications

The Artificial Intelligence in Digital Radiology: Part 1: The Challenges, Acceptance and Consensus

The Artificial Intelligence in Digital Radiology: Part 1: The Challenges, Acceptance and Consensus

The Regulation of Artificial Intelligence in Digital Radiology in the Scientific Literature: A Narrative Review of Reviews

The transformational potential of molecular radiomics

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