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  1. Article ; Online: Artificial intelligence and machine learning in axial spondyloarthritis.

    Adams, Lisa C / Bressem, Keno K / Poddubnyy, Denis

    Current opinion in rheumatology

    2024  

    Abstract: Purpose of review: To evaluate the current applications and prospects of artificial intelligence and machine learning in diagnosing and managing axial spondyloarthritis (axSpA), focusing on their role in medical imaging, predictive modelling, and ... ...

    Abstract Purpose of review: To evaluate the current applications and prospects of artificial intelligence and machine learning in diagnosing and managing axial spondyloarthritis (axSpA), focusing on their role in medical imaging, predictive modelling, and patient monitoring.
    Recent findings: Artificial intelligence, particularly deep learning, is showing promise in diagnosing axSpA assisting with X-ray, computed tomography (CT) and MRI analyses, with some models matching or outperforming radiologists in detecting sacroiliitis and markers. Moreover, it is increasingly being used in predictive modelling of disease progression and personalized treatment, and could aid risk assessment, treatment response and clinical subtype identification. Variable study designs, sample sizes and the predominance of retrospective, single-centre studies still limit the generalizability of results.
    Summary: Artificial intelligence technologies have significant potential to advance the diagnosis and treatment of axSpA, providing more accurate, efficient and personalized healthcare solutions. However, their integration into clinical practice requires rigorous validation, ethical and legal considerations, and comprehensive training for healthcare professionals. Future advances in artificial intelligence could complement clinical expertise and improve patient care through improved diagnostic accuracy and tailored therapeutic strategies, but the challenge remains to ensure that these technologies are validated in prospective multicentre trials and ethically integrated into patient care.
    Language English
    Publishing date 2024-03-28
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1045317-9
    ISSN 1531-6963 ; 1040-8711
    ISSN (online) 1531-6963
    ISSN 1040-8711
    DOI 10.1097/BOR.0000000000001015
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 3028: Show issues Location:
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    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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  2. Article ; Online: Spotlight on the biomedical ethical integration of AI in medical education - Response to: 'An explorative assessment of ChatGPT as an aid in medical education: Use it with caution'.

    Busch, Felix / Adams, Lisa C / Bressem, Keno K

    Medical teacher

    2023  , Page(s) 1

    Language English
    Publishing date 2023-12-17
    Publishing country England
    Document type Journal Article
    ZDB-ID 424426-6
    ISSN 1466-187X ; 0142-159X
    ISSN (online) 1466-187X
    ISSN 0142-159X
    DOI 10.1080/0142159X.2023.2293655
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Editorial for "An Unsupervised Deep Learning Approach for Dynamic-Exponential Intravoxel Incoherent Motion MRI Modeling and Parameter Estimation in the Liver".

    Adams, Lisa C / Bressem, Keno K

    Journal of magnetic resonance imaging : JMRI

    2022  Volume 56, Issue 3, Page(s) 860–861

    MeSH term(s) Deep Learning ; Diffusion Magnetic Resonance Imaging ; Humans ; Liver/diagnostic imaging ; Magnetic Resonance Imaging ; Motion ; Reproducibility of Results
    Language English
    Publishing date 2022-01-21
    Publishing country United States
    Document type Editorial ; Comment
    ZDB-ID 1146614-5
    ISSN 1522-2586 ; 1053-1807
    ISSN (online) 1522-2586
    ISSN 1053-1807
    DOI 10.1002/jmri.28075
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    Zs.A 3648: Show issues Location:
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    Zs.MO 236: Show issues

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  4. Article ; Online: Comparative Analysis of Multimodal Large Language Model Performance on Clinical Vignette Questions.

    Han, Tianyu / Adams, Lisa C / Bressem, Keno K / Busch, Felix / Nebelung, Sven / Truhn, Daniel

    JAMA

    2024  Volume 331, Issue 15, Page(s) 1320–1321

    MeSH term(s) Artificial Intelligence ; Diagnostic Imaging ; Language ; Medical History Taking
    Language English
    Publishing date 2024-03-18
    Publishing country United States
    Document type Comparative Study ; Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2958-0
    ISSN 1538-3598 ; 0254-9077 ; 0002-9955 ; 0098-7484
    ISSN (online) 1538-3598
    ISSN 0254-9077 ; 0002-9955 ; 0098-7484
    DOI 10.1001/jama.2023.27861
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  5. Article ; Online: Biomedical Ethical Aspects Towards the Implementation of Artificial Intelligence in Medical Education.

    Busch, Felix / Adams, Lisa C / Bressem, Keno K

    Medical science educator

    2023  Volume 33, Issue 4, Page(s) 1007–1012

    Abstract: The increasing use of artificial intelligence (AI) in medicine is associated with new ethical challenges and responsibilities. However, special considerations and concerns should be addressed when integrating AI applications into medical education, where ...

    Abstract The increasing use of artificial intelligence (AI) in medicine is associated with new ethical challenges and responsibilities. However, special considerations and concerns should be addressed when integrating AI applications into medical education, where healthcare, AI, and education ethics collide. This commentary explores the biomedical ethical responsibilities of medical institutions in incorporating AI applications into medical education by identifying potential concerns and limitations, with the goal of implementing applicable recommendations. The recommendations presented are intended to assist in developing institutional guidelines for the ethical use of AI for medical educators and students.
    Language English
    Publishing date 2023-06-07
    Publishing country United States
    Document type Editorial
    ISSN 2156-8650
    ISSN (online) 2156-8650
    DOI 10.1007/s40670-023-01815-x
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  6. Article ; Online: Artificial intelligence to analyze magnetic resonance imaging in rheumatology.

    Adams, Lisa C / Bressem, Keno K / Ziegeler, Katharina / Vahldiek, Janis L / Poddubnyy, Denis

    Joint bone spine

    2023  Volume 91, Issue 3, Page(s) 105651

    Abstract: Rheumatic disorders present a global health challenge, marked by inflammation and damage to joints, bones, and connective tissues. Accurate, timely diagnosis and appropriate management are crucial for favorable patient outcomes. Magnetic resonance ... ...

    Abstract Rheumatic disorders present a global health challenge, marked by inflammation and damage to joints, bones, and connective tissues. Accurate, timely diagnosis and appropriate management are crucial for favorable patient outcomes. Magnetic resonance imaging (MRI) has become indispensable in rheumatology, but interpretation remains laborious and variable. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offers a means to improve and advance MRI analysis. This review examines current AI applications in rheumatology MRI analysis, addressing diagnostic support, disease classification, activity assessment, and progression monitoring. AI demonstrates promise, with high sensitivity, specificity, and accuracy, achieving or surpassing expert performance. The review also discusses clinical implementation challenges and future research directions to enhance rheumatic disease diagnosis and management.
    Language English
    Publishing date 2023-10-04
    Publishing country France
    Document type Journal Article
    ZDB-ID 2020487-5
    ISSN 1778-7254 ; 1297-319X
    ISSN (online) 1778-7254
    ISSN 1297-319X
    DOI 10.1016/j.jbspin.2023.105651
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    Zs.A 4590: Show issues Location:
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    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  7. Article ; Online: Leveraging GPT-4 for Post Hoc Transformation of Free-text Radiology Reports into Structured Reporting: A Multilingual Feasibility Study.

    Adams, Lisa C / Truhn, Daniel / Busch, Felix / Kader, Avan / Niehues, Stefan M / Makowski, Marcus R / Bressem, Keno K

    Radiology

    2023  Volume 307, Issue 4, Page(s) e230725

    MeSH term(s) Humans ; Feasibility Studies ; Radiology ; Radiography ; Radiology Information Systems
    Language English
    Publishing date 2023-04-04
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 80324-8
    ISSN 1527-1315 ; 0033-8419
    ISSN (online) 1527-1315
    ISSN 0033-8419
    DOI 10.1148/radiol.230725
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    Ue I Zs.106: Show issues Location:
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  8. Article: Two-Stage Deep Learning Model for Automated Segmentation and Classification of Splenomegaly.

    Meddeb, Aymen / Kossen, Tabea / Bressem, Keno K / Molinski, Noah / Hamm, Bernd / Nagel, Sebastian N

    Cancers

    2022  Volume 14, Issue 22

    Abstract: Splenomegaly is a common cross-sectional imaging finding with a variety of differential diagnoses. This study aimed to evaluate whether a deep learning model could automatically segment the spleen and identify the cause of splenomegaly in patients with ... ...

    Abstract Splenomegaly is a common cross-sectional imaging finding with a variety of differential diagnoses. This study aimed to evaluate whether a deep learning model could automatically segment the spleen and identify the cause of splenomegaly in patients with cirrhotic portal hypertension versus patients with lymphoma disease. This retrospective study included 149 patients with splenomegaly on computed tomography (CT) images (77 patients with cirrhotic portal hypertension, 72 patients with lymphoma) who underwent a CT scan between October 2020 and July 2021. The dataset was divided into a training (
    Language English
    Publishing date 2022-11-08
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2527080-1
    ISSN 2072-6694
    ISSN 2072-6694
    DOI 10.3390/cancers14225476
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  9. Article ; Online: What Does DALL-E 2 Know About Radiology?

    Adams, Lisa C / Busch, Felix / Truhn, Daniel / Makowski, Marcus R / Aerts, Hugo J W L / Bressem, Keno K

    Journal of medical Internet research

    2023  Volume 25, Page(s) e43110

    Abstract: Generative models, such as DALL-E 2 (OpenAI), could represent promising future tools for image generation, augmentation, and manipulation for artificial intelligence research in radiology, provided that these models have sufficient medical domain ... ...

    Abstract Generative models, such as DALL-E 2 (OpenAI), could represent promising future tools for image generation, augmentation, and manipulation for artificial intelligence research in radiology, provided that these models have sufficient medical domain knowledge. Herein, we show that DALL-E 2 has learned relevant representations of x-ray images, with promising capabilities in terms of zero-shot text-to-image generation of new images, the continuation of an image beyond its original boundaries, and the removal of elements; however, its capabilities for the generation of images with pathological abnormalities (eg, tumors, fractures, and inflammation) or computed tomography, magnetic resonance imaging, or ultrasound images are still limited. The use of generative models for augmenting and generating radiological data thus seems feasible, even if the further fine-tuning and adaptation of these models to their respective domains are required first.
    MeSH term(s) Humans ; Artificial Intelligence ; Tomography, X-Ray Computed/methods ; Magnetic Resonance Imaging/methods ; Ultrasonography ; Radiology
    Language English
    Publishing date 2023-03-16
    Publishing country Canada
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2028830-X
    ISSN 1438-8871 ; 1438-8871
    ISSN (online) 1438-8871
    ISSN 1438-8871
    DOI 10.2196/43110
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article: Mapping gender and geographic diversity in artificial intelligence research: Editor representation in leading computer science journals.

    Busch, Felix / Keller, Sarah / Rueger, Christopher / Kader, Avan / Ziegeler, Katharina / Bressem, Keno K / Adams, Lisa C

    Acta radiologica open

    2023  Volume 12, Issue 10, Page(s) 20584601231213740

    Abstract: Background: The growing role of artificial intelligence (AI) in healthcare, particularly radiology, requires its unbiased and fair development and implementation, starting with the constitution of the scientific community.: Purpose: To examine the ... ...

    Abstract Background: The growing role of artificial intelligence (AI) in healthcare, particularly radiology, requires its unbiased and fair development and implementation, starting with the constitution of the scientific community.
    Purpose: To examine the gender and country distribution among academic editors in leading computer science and AI journals.
    Material and methods: This cross-sectional study analyzed the gender and country distribution among editors-in-chief, senior, and associate editors in all 75 Q1 computer science and AI journals in the Clarivate Journal Citations Report and SCImago Journal Ranking 2022. Gender was determined using an open-source algorithm (Gender Guesser™), selecting the gender with the highest calibrated probability.
    Result: Among 4,948 editorial board members, women were underrepresented in all positions (editors-in-chief/senior editors/associate editors: 14%/18%/17%). The proportion of women correlated positively with the SCImago Journal Rank indicator (ρ = 0.329;
    Conclusion: Our results highlight gender and geographic disparities on leading computer science and AI journal editorial boards, with women being underrepresented in all positions and a disproportional relationship between the Global North and South.
    Language English
    Publishing date 2023-11-28
    Publishing country England
    Document type Journal Article
    ZDB-ID 2818429-4
    ISSN 2058-4601
    ISSN 2058-4601
    DOI 10.1177/20584601231213740
    Database MEDical Literature Analysis and Retrieval System OnLINE

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