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  1. Article: How AI should be used in radiology: assessing ambiguity and completeness of intended use statements of commercial AI products.

    van Leeuwen, Kicky G / Hedderich, Dennis M / Harvey, Hugh / Schalekamp, Steven

    Insights into imaging

    2024  Volume 15, Issue 1, Page(s) 51

    Abstract: Background: Intended use statements (IUSs) are mandatory to obtain regulatory clearance for artificial intelligence (AI)-based medical devices in the European Union. In order to guide the safe use of AI-based medical devices, IUSs need to contain ... ...

    Abstract Background: Intended use statements (IUSs) are mandatory to obtain regulatory clearance for artificial intelligence (AI)-based medical devices in the European Union. In order to guide the safe use of AI-based medical devices, IUSs need to contain comprehensive and understandable information. This study analyzes the IUSs of CE-marked AI products listed on AIforRadiology.com for ambiguity and completeness.
    Methods: We retrieved 157 IUSs of CE-marked AI products listed on AIforRadiology.com in September 2022. Duplicate products (n = 1), discontinued products (n = 3), and duplicate statements (n = 14) were excluded. The resulting IUSs were assessed for the presence of 6 items: medical indication, part of the body, patient population, user profile, use environment, and operating principle. Disclaimers, defined as contra-indications or warnings in the IUS, were identified and compared with claims.
    Results: Of 139 AI products, the majority (n = 78) of IUSs mentioned 3 or less items. IUSs of only 7 products mentioned all 6 items. The intended body part (n = 115) and the operating principle (n = 116) were the most frequently mentioned components, while the intended use environment (n = 24) and intended patient population (n = 29) were mentioned less frequently. Fifty-six statements contained disclaimers that conflicted with the claims in 13 cases.
    Conclusion: The majority of IUSs of CE-marked AI-based medical devices lack substantial information and, in few cases, contradict the claims of the product.
    Critical relevance statement: To ensure correct usage and to avoid off-label use or foreseeable misuse of AI-based medical devices in radiology, manufacturers are encouraged to provide more comprehensive and less ambiguous intended use statements.
    Key points: • Radiologists must know AI products' intended use to avoid off-label use or misuse. • Ninety-five percent (n = 132/139) of the intended use statements analyzed were incomplete. • Nine percent (n = 13) of the intended use statements held disclaimers contradicting the claim of the AI product. • Manufacturers and regulatory bodies must ensure that intended use statements are comprehensive.
    Language English
    Publishing date 2024-02-16
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 2543323-4
    ISSN 1869-4101
    ISSN 1869-4101
    DOI 10.1186/s13244-024-01616-9
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective.

    Schalekamp, Steven / Klein, Willemijn M / van Leeuwen, Kicky G

    Pediatric radiology

    2021  Volume 52, Issue 11, Page(s) 2120–2130

    Abstract: Artificial intelligence (AI) applications for chest radiography and chest CT are among the most developed applications in radiology. More than 40 certified AI products are available for chest radiography or chest CT. These AI products cover a wide range ... ...

    Abstract Artificial intelligence (AI) applications for chest radiography and chest CT are among the most developed applications in radiology. More than 40 certified AI products are available for chest radiography or chest CT. These AI products cover a wide range of abnormalities, including pneumonia, pneumothorax and lung cancer. Most applications are aimed at detecting disease, complemented by products that characterize or quantify tissue. At present, none of the thoracic AI products is specifically designed for the pediatric population. However, some products developed to detect tuberculosis in adults are also applicable to children. Software is under development to detect early changes of cystic fibrosis on chest CT, which could be an interesting application for pediatric radiology. In this review, we give an overview of current AI products in thoracic radiology and cover recent literature about AI in chest radiography, with a focus on pediatric radiology. We also discuss possible pediatric applications.
    MeSH term(s) Adult ; Artificial Intelligence ; Child ; Humans ; Radiography, Thoracic ; Radiology ; Thorax ; Tomography, X-Ray Computed
    Language English
    Publishing date 2021-09-01
    Publishing country Germany
    Document type Journal Article ; Review
    ZDB-ID 124459-0
    ISSN 1432-1998 ; 0301-0449
    ISSN (online) 1432-1998
    ISSN 0301-0449
    DOI 10.1007/s00247-021-05146-0
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

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    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  3. Article ; Online: Duration and accuracy of automated stroke CT workflow with AI-supported intracranial large vessel occlusion detection.

    Temmen, Sander E / Becks, Marinus J / Schalekamp, Steven / van Leeuwen, Kicky G / Meijer, Frederick J A

    Scientific reports

    2023  Volume 13, Issue 1, Page(s) 12551

    Abstract: The Automation Platform (AP) is a software platform to support the workflow of radiologists and includes a stroke CT package with integrated artificial intelligence (AI) based tools. The aim of this study was to evaluate the diagnostic performance of the ...

    Abstract The Automation Platform (AP) is a software platform to support the workflow of radiologists and includes a stroke CT package with integrated artificial intelligence (AI) based tools. The aim of this study was to evaluate the diagnostic performance of the AP for the detection of intracranial large vessel occlusions (LVO) on conventional CT angiography (CTA), and the duration of CT processing in a cohort of acute stroke patients. The diagnostic performance for intracranial LVO detection on CTA by the AP was evaluated in a retrospective cohort of 100 acute stroke patients and compared to the diagnostic performance of five radiologists with different levels of experience. The reference standard was set by an independent neuroradiologist, with access to the readings of the different radiologists, clinical data, and follow-up. The data processing time of the AP for ICH detection on non-contrast CT, LVO detection on CTA, and the processing of CTP maps was assessed in a subset 60 patients of the retrospective cohort. This was compared to 13 radiologists, who were prospectively timed for the processing and reading of 21 stroke CTs. The AP showed shorter processing time of CTA (mean 60 versus 395 s) and CTP (mean 196 versus 243-349 s) as compared to radiologists, but showed lower sensitivity for LVO detection (sensitivity 77% of the AP vs mean sensitivity 87% of radiologists). If the AP would have been used as a stand-alone system, 1 ICA occlusion, 2 M1 occlusions and 8 M2 occlusions would have been missed, which would be eligible for mechanical thrombectomy. In conclusion, the AP showed shorter processing time of CTA and CTP as compared with radiologists, which illustrates the potential of the AP to speed-up the diagnostic work-up. However, its performance for LVO detection was lower as compared with radiologists, especially for M2 vessel occlusions.
    MeSH term(s) Humans ; Artificial Intelligence ; Retrospective Studies ; Workflow ; Cerebral Angiography ; Stroke/diagnostic imaging ; Computed Tomography Angiography ; Brain Ischemia
    Language English
    Publishing date 2023-08-02
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-023-39831-x
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Explainable emphysema detection on chest radiographs with deep learning.

    Çallı, Erdi / Murphy, Keelin / Scholten, Ernst T / Schalekamp, Steven / van Ginneken, Bram

    PloS one

    2022  Volume 17, Issue 7, Page(s) e0267539

    Abstract: We propose a deep learning system to automatically detect four explainable emphysema signs on frontal and lateral chest radiographs. Frontal and lateral chest radiographs from 3000 studies were retrospectively collected. Two radiologists annotated these ... ...

    Abstract We propose a deep learning system to automatically detect four explainable emphysema signs on frontal and lateral chest radiographs. Frontal and lateral chest radiographs from 3000 studies were retrospectively collected. Two radiologists annotated these with 4 radiological signs of pulmonary emphysema identified from the literature. A patient with ≥2 of these signs present is considered emphysema positive. Using separate deep learning systems for frontal and lateral images we predict the presence of each of the four visual signs and use these to determine emphysema positivity. The ROC and AUC results on a set of 422 held-out cases, labeled by both radiologists, are reported. Comparison with a black-box model which predicts emphysema without the use of explainable visual features is made on the annotations from both radiologists, as well as the subset that they agreed on. DeLong's test is used to compare with the black-box model ROC and McNemar's test to compare with radiologist performance. In 422 test cases, emphysema positivity was predicted with AUCs of 0.924 and 0.946 using the reference standard from each radiologist separately. Setting model sensitivity equivalent to that of the second radiologist, our model has a comparable specificity (p = 0.880 and p = 0.143 for each radiologist respectively). Our method is comparable with the black-box model with AUCs of 0.915 (p = 0.407) and 0.935 (p = 0.291), respectively. On the 370 cases where both radiologists agreed (53 positives), our model achieves an AUC of 0.981, again comparable to the black-box model AUC of 0.972 (p = 0.289). Our proposed method can predict emphysema positivity on chest radiographs as well as a radiologist or a comparable black-box method. It additionally produces labels for four visual signs to ensure the explainability of the result. The dataset is publicly available at https://doi.org/10.5281/zenodo.6373392.
    MeSH term(s) Deep Learning ; Emphysema/diagnostic imaging ; Humans ; Pulmonary Emphysema/diagnostic imaging ; Radiography ; Radiography, Thoracic/methods ; Radiologists ; Retrospective Studies
    Language English
    Publishing date 2022-07-28
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0267539
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Clinical use of artificial intelligence products for radiology in the Netherlands between 2020 and 2022.

    van Leeuwen, Kicky G / de Rooij, Maarten / Schalekamp, Steven / van Ginneken, Bram / Rutten, Matthieu J C M

    European radiology

    2023  Volume 34, Issue 1, Page(s) 348–354

    Abstract: Objectives: To map the clinical use of CE-marked artificial intelligence (AI)-based software in radiology departments in the Netherlands (n = 69) between 2020 and 2022.: Materials and methods: Our AI network (one radiologist or AI representative per ... ...

    Abstract Objectives: To map the clinical use of CE-marked artificial intelligence (AI)-based software in radiology departments in the Netherlands (n = 69) between 2020 and 2022.
    Materials and methods: Our AI network (one radiologist or AI representative per Dutch hospital organization) received a questionnaire each spring from 2020 to 2022 about AI product usage, financing, and obstacles to adoption. Products that were not listed on www.AIforRadiology.com by July 2022 were excluded from the analysis.
    Results: The number of respondents was 43 in 2020, 36 in 2021, and 33 in 2022. The number of departments using AI has been growing steadily (2020: 14, 2021: 19, 2022: 23). The diversity (2020: 7, 2021: 18, 2022: 34) and the number of total implementations (2020: 19, 2021: 38, 2022: 68) has rapidly increased. Seven implementations were discontinued in 2022. Four hospital organizations said to use an AI platform or marketplace for the deployment of AI solutions. AI is mostly used to support chest CT (17), neuro CT (17), and musculoskeletal radiograph (12) analysis. The budget for AI was reserved in 13 of the responding centers in both 2021 and 2022. The most important obstacles to the adoption of AI remained costs and IT integration. Of the respondents, 28% stated that the implemented AI products realized health improvement and 32% assumed both health improvement and cost savings.
    Conclusion: The adoption of AI products in radiology departments in the Netherlands is showing common signs of a developing market. The major obstacles to reaching widespread adoption are a lack of financial resources and IT integration difficulties.
    Clinical relevance statement: The clinical impact of AI starts with its adoption in daily clinical practice. Increased transparency around AI products being adopted, implementation obstacles, and impact may inspire increased collaboration and improved decision-making around the implementation and financing of AI products.
    Key points: • The adoption of artificial intelligence products for radiology has steadily increased since 2020 to at least a third of the centers using AI in clinical practice in the Netherlands in 2022. • The main areas in which artificial intelligence products are used are lung nodule detection on CT, aided stroke diagnosis, and bone age prediction. • The majority of respondents experienced added value (decreased costs and/or improved outcomes) from using artificial intelligence-based software; however, major obstacles to adoption remain the costs and IT-related difficulties.
    MeSH term(s) Humans ; Artificial Intelligence ; Netherlands ; Radiology ; Radiography ; Radiologists
    Language English
    Publishing date 2023-07-29
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 1085366-2
    ISSN 1432-1084 ; 0938-7994 ; 1613-3749
    ISSN (online) 1432-1084
    ISSN 0938-7994 ; 1613-3749
    DOI 10.1007/s00330-023-09991-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 3300: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  6. Article ; Online: Explainable emphysema detection on chest radiographs with deep learning.

    Erdi Çallı / Keelin Murphy / Ernst T Scholten / Steven Schalekamp / Bram van Ginneken

    PLoS ONE, Vol 17, Iss 7, p e

    2022  Volume 0267539

    Abstract: We propose a deep learning system to automatically detect four explainable emphysema signs on frontal and lateral chest radiographs. Frontal and lateral chest radiographs from 3000 studies were retrospectively collected. Two radiologists annotated these ... ...

    Abstract We propose a deep learning system to automatically detect four explainable emphysema signs on frontal and lateral chest radiographs. Frontal and lateral chest radiographs from 3000 studies were retrospectively collected. Two radiologists annotated these with 4 radiological signs of pulmonary emphysema identified from the literature. A patient with ≥2 of these signs present is considered emphysema positive. Using separate deep learning systems for frontal and lateral images we predict the presence of each of the four visual signs and use these to determine emphysema positivity. The ROC and AUC results on a set of 422 held-out cases, labeled by both radiologists, are reported. Comparison with a black-box model which predicts emphysema without the use of explainable visual features is made on the annotations from both radiologists, as well as the subset that they agreed on. DeLong's test is used to compare with the black-box model ROC and McNemar's test to compare with radiologist performance. In 422 test cases, emphysema positivity was predicted with AUCs of 0.924 and 0.946 using the reference standard from each radiologist separately. Setting model sensitivity equivalent to that of the second radiologist, our model has a comparable specificity (p = 0.880 and p = 0.143 for each radiologist respectively). Our method is comparable with the black-box model with AUCs of 0.915 (p = 0.407) and 0.935 (p = 0.291), respectively. On the 370 cases where both radiologists agreed (53 positives), our model achieves an AUC of 0.981, again comparable to the black-box model AUC of 0.972 (p = 0.289). Our proposed method can predict emphysema positivity on chest radiographs as well as a radiologist or a comparable black-box method. It additionally produces labels for four visual signs to ensure the explainability of the result. The dataset is publicly available at https://doi.org/10.5281/zenodo.6373392.
    Keywords Medicine ; R ; Science ; Q
    Language English
    Publishing date 2022-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Nodule detection and generation on chest X-rays: NODE21 Challenge.

    Sogancioglu, Ecem / Van Ginneken, Bram / Behrendt, Finn / Bengs, Marcel / Schlaefer, Alexander / Radu, Miron / Xu, Di / Sheng, Ke / Scalzo, Fabien / Marcus, Eric / Papa, Samuele / Teuwen, Jonas / Scholten, Ernst Th / Schalekamp, Steven / Hendrix, Nils / Jacobs, Colin / Hendrix, Ward / Sanchez, Clara I / Murphy, Keelin

    IEEE transactions on medical imaging

    2024  Volume PP

    Abstract: Pulmonary nodules may be an early manifestation of lung cancer, the leading cause of cancer-related deaths among both men and women. Numerous studies have established that deep learning methods can yield high-performance levels in the detection of lung ... ...

    Abstract Pulmonary nodules may be an early manifestation of lung cancer, the leading cause of cancer-related deaths among both men and women. Numerous studies have established that deep learning methods can yield high-performance levels in the detection of lung nodules in chest X-rays. However, the lack of gold-standard public datasets slows down the progression of the research and prevents benchmarking of methods for this task. To address this, we organized a public research challenge, NODE21, aimed at the detection and generation of lung nodules in chest X-rays. While the detection track assesses state-of-the-art nodule detection systems, the generation track determines the utility of nodule generation algorithms to augment training data and hence improve the performance of the detection systems. This paper summarizes the results of the NODE21 challenge and performs extensive additional experiments to examine the impact of the synthetically generated nodule training images on the detection algorithm performance.
    Language English
    Publishing date 2024-03-26
    Publishing country United States
    Document type Journal Article
    ZDB-ID 622531-7
    ISSN 1558-254X ; 0278-0062
    ISSN (online) 1558-254X
    ISSN 0278-0062
    DOI 10.1109/TMI.2024.3382042
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2546: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  8. Article ; Online: Deep learning for the detection of benign and malignant pulmonary nodules in non-screening chest CT scans.

    Hendrix, Ward / Hendrix, Nils / Scholten, Ernst T / Mourits, Mariëlle / Trap-de Jong, Joline / Schalekamp, Steven / Korst, Mike / van Leuken, Maarten / van Ginneken, Bram / Prokop, Mathias / Rutten, Matthieu / Jacobs, Colin

    Communications medicine

    2023  Volume 3, Issue 1, Page(s) 156

    Abstract: Background: Outside a screening program, early-stage lung cancer is generally diagnosed after the detection of incidental nodules in clinically ordered chest CT scans. Despite the advances in artificial intelligence (AI) systems for lung cancer ... ...

    Abstract Background: Outside a screening program, early-stage lung cancer is generally diagnosed after the detection of incidental nodules in clinically ordered chest CT scans. Despite the advances in artificial intelligence (AI) systems for lung cancer detection, clinical validation of these systems is lacking in a non-screening setting.
    Method: We developed a deep learning-based AI system and assessed its performance for the detection of actionable benign nodules (requiring follow-up), small lung cancers, and pulmonary metastases in CT scans acquired in two Dutch hospitals (internal and external validation). A panel of five thoracic radiologists labeled all nodules, and two additional radiologists verified the nodule malignancy status and searched for any missed cancers using data from the national Netherlands Cancer Registry. The detection performance was evaluated by measuring the sensitivity at predefined false positive rates on a free receiver operating characteristic curve and was compared with the panel of radiologists.
    Results: On the external test set (100 scans from 100 patients), the sensitivity of the AI system for detecting benign nodules, primary lung cancers, and metastases is respectively 94.3% (82/87, 95% CI: 88.1-98.8%), 96.9% (31/32, 95% CI: 91.7-100%), and 92.0% (104/113, 95% CI: 88.5-95.5%) at a clinically acceptable operating point of 1 false positive per scan (FP/s). These sensitivities are comparable to or higher than the radiologists, albeit with a slightly higher FP/s (average difference of 0.6).
    Conclusions: The AI system reliably detects benign and malignant pulmonary nodules in clinically indicated CT scans and can potentially assist radiologists in this setting.
    Language English
    Publishing date 2023-10-27
    Publishing country England
    Document type Journal Article
    ISSN 2730-664X
    ISSN (online) 2730-664X
    DOI 10.1038/s43856-023-00388-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: How does artificial intelligence in radiology improve efficiency and health outcomes?

    van Leeuwen, Kicky G / de Rooij, Maarten / Schalekamp, Steven / van Ginneken, Bram / Rutten, Matthieu J C M

    Pediatric radiology

    2021  Volume 52, Issue 11, Page(s) 2087–2093

    Abstract: Since the introduction of artificial intelligence (AI) in radiology, the promise has been that it will improve health care and reduce costs. Has AI been able to fulfill that promise? We describe six clinical objectives that can be supported by AI: a more ...

    Abstract Since the introduction of artificial intelligence (AI) in radiology, the promise has been that it will improve health care and reduce costs. Has AI been able to fulfill that promise? We describe six clinical objectives that can be supported by AI: a more efficient workflow, shortened reading time, a reduction of dose and contrast agents, earlier detection of disease, improved diagnostic accuracy and more personalized diagnostics. We provide examples of use cases including the available scientific evidence for its impact based on a hierarchical model of efficacy. We conclude that the market is still maturing and little is known about the contribution of AI to clinical practice. More real-world monitoring of AI in clinical practice is expected to aid in determining the value of AI and making informed decisions on development, procurement and reimbursement.
    MeSH term(s) Artificial Intelligence ; Contrast Media ; Humans ; Outcome Assessment, Health Care ; Radiography ; Radiology
    Chemical Substances Contrast Media
    Language English
    Publishing date 2021-06-12
    Publishing country Germany
    Document type Journal Article ; Review
    ZDB-ID 124459-0
    ISSN 1432-1998 ; 0301-0449
    ISSN (online) 1432-1998
    ISSN 0301-0449
    DOI 10.1007/s00247-021-05114-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 927: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  10. Article ; Online: Artificial intelligence in radiology: 100 commercially available products and their scientific evidence.

    van Leeuwen, Kicky G / Schalekamp, Steven / Rutten, Matthieu J C M / van Ginneken, Bram / de Rooij, Maarten

    European radiology

    2021  Volume 31, Issue 6, Page(s) 3797–3804

    Abstract: Objectives: Map the current landscape of commercially available artificial intelligence (AI) software for radiology and review the availability of their scientific evidence.: Methods: We created an online overview of CE-marked AI software products ... ...

    Abstract Objectives: Map the current landscape of commercially available artificial intelligence (AI) software for radiology and review the availability of their scientific evidence.
    Methods: We created an online overview of CE-marked AI software products for clinical radiology based on vendor-supplied product specifications ( www.aiforradiology.com ). Characteristics such as modality, subspeciality, main task, regulatory information, deployment, and pricing model were retrieved. We conducted an extensive literature search on the available scientific evidence of these products. Articles were classified according to a hierarchical model of efficacy.
    Results: The overview included 100 CE-marked AI products from 54 different vendors. For 64/100 products, there was no peer-reviewed evidence of its efficacy. We observed a large heterogeneity in deployment methods, pricing models, and regulatory classes. The evidence of the remaining 36/100 products comprised 237 papers that predominantly (65%) focused on diagnostic accuracy (efficacy level 2). From the 100 products, 18 had evidence that regarded level 3 or higher, validating the (potential) impact on diagnostic thinking, patient outcome, or costs. Half of the available evidence (116/237) were independent and not (co-)funded or (co-)authored by the vendor.
    Conclusions: Even though the commercial supply of AI software in radiology already holds 100 CE-marked products, we conclude that the sector is still in its infancy. For 64/100 products, peer-reviewed evidence on its efficacy is lacking. Only 18/100 AI products have demonstrated (potential) clinical impact.
    Key points: • Artificial intelligence in radiology is still in its infancy even though already 100 CE-marked AI products are commercially available. • Only 36 out of 100 products have peer-reviewed evidence of which most studies demonstrate lower levels of efficacy. • There is a wide variety in deployment strategies, pricing models, and CE marking class of AI products for radiology.
    MeSH term(s) Artificial Intelligence ; Humans ; Radiography ; Radiology ; Software
    Language English
    Publishing date 2021-04-15
    Publishing country Germany
    Document type Journal Article ; Review
    ZDB-ID 1085366-2
    ISSN 1432-1084 ; 0938-7994 ; 1613-3749
    ISSN (online) 1432-1084
    ISSN 0938-7994 ; 1613-3749
    DOI 10.1007/s00330-021-07892-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

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    ab Jg. 2022: Lesesaal (EG)

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