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  1. Article ; Online: Conflicts of Interest in Radiology Publishing.

    Doo, Florence X / Siegel, Eliot

    Journal of the American College of Radiology : JACR

    2024  

    Abstract: Lack of disclosure of Conflicts of Interest (COI) in radiology research can undermine trust in medical recommendations and patient care. A recent study found significant discrepancies between disclosed COIs and those listed in the Open Payments Database ( ...

    Abstract Lack of disclosure of Conflicts of Interest (COI) in radiology research can undermine trust in medical recommendations and patient care. A recent study found significant discrepancies between disclosed COIs and those listed in the Open Payments Database (OPD). This commentary discusses the importance of transparency in financial and nonfinancial COIs, the implications of undisclosed COIs on research integrity and clinical decision-making, and the challenges and controversies surrounding current disclosure practices. The field of radiology should discuss and update COI management and ethical standards, for more practical accountability in radiology publishing.
    Language English
    Publishing date 2024-03-23
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2274861-1
    ISSN 1558-349X ; 1546-1440
    ISSN (online) 1558-349X
    ISSN 1546-1440
    DOI 10.1016/j.jacr.2024.03.014
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Beyond the

    Doo, Florence X / Parekh, Vishwa S

    AJR. American journal of roentgenology

    2023  

    Language English
    Publishing date 2023-12-20
    Publishing country United States
    Document type Journal Article
    ZDB-ID 82076-3
    ISSN 1546-3141 ; 0361-803X ; 0092-5381
    ISSN (online) 1546-3141
    ISSN 0361-803X ; 0092-5381
    DOI 10.2214/AJR.23.30696
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  3. Article ; Online: Building Diversity, Equity, and Inclusion Within Radiology Artificial Intelligence: Representation Matters, From Data to the Workforce.

    Doo, Florence X / McGinty, Geraldine B

    Journal of the American College of Radiology : JACR

    2023  Volume 20, Issue 9, Page(s) 852–856

    Abstract: Diversity, equity, and inclusion (DEI) is both a critical ingredient and moral imperative in shaping the future of radiology artificial intelligence (AI) for improved patient care, from design to deployment. At the design level: Potential biases and ... ...

    Abstract Diversity, equity, and inclusion (DEI) is both a critical ingredient and moral imperative in shaping the future of radiology artificial intelligence (AI) for improved patient care, from design to deployment. At the design level: Potential biases and discrimination within data sets results in inaccurate radiology AI models, and there is an urgent need to purposefully embed DEI principles throughout the AI development and implementation process. At the deployment level: Diverse representation in radiology AI leadership, research, and career development is necessary to avoid worsening structural and historical health inequities. To create an inclusive and equitable AI-enabled future in healthcare, a DEI radiology AI leadership training program may be needed to cultivate a diverse and sustainable pipeline of leaders in the field.
    Language English
    Publishing date 2023-07-14
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 2274861-1
    ISSN 1558-349X ; 1546-1440
    ISSN (online) 1558-349X
    ISSN 1546-1440
    DOI 10.1016/j.jacr.2023.06.014
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  4. Article ; Online: Evaluation of Multimodal ChatGPT (GPT-4V) in Describing Mammography Image Features.

    Haver, Hana L / Bahl, Manisha / Doo, Florence X / Kamel, Peter I / Parekh, Vishwa S / Jeudy, Jean / Yi, Paul H

    Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes

    2024  , Page(s) 8465371241247043

    Language English
    Publishing date 2024-04-06
    Publishing country United States
    Document type Letter
    ZDB-ID 418190-6
    ISSN 1488-2361 ; 0846-5371 ; 0008-2902
    ISSN (online) 1488-2361
    ISSN 0846-5371 ; 0008-2902
    DOI 10.1177/08465371241247043
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  5. Article ; Online: Proceedings From the 2022 ACR-RSNA Workshop on Safety, Effectiveness, Reliability, and Transparency in AI.

    Larson, David B / Doo, Florence X / Allen, Bibb / Mongan, John / Flanders, Adam E / Wald, Christoph

    Journal of the American College of Radiology : JACR

    2024  

    Abstract: Despite the surge in artificial intelligence (AI) development for health care applications, particularly for medical imaging applications, there has been limited adoption of such AI tools into clinical practice. During a 1-day workshop in November 2022, ... ...

    Abstract Despite the surge in artificial intelligence (AI) development for health care applications, particularly for medical imaging applications, there has been limited adoption of such AI tools into clinical practice. During a 1-day workshop in November 2022, co-organized by the ACR and the RSNA, participants outlined experiences and problems with implementing AI in clinical practice, defined the needs of various stakeholders in the AI ecosystem, and elicited potential solutions and strategies related to the safety, effectiveness, reliability, and transparency of AI algorithms. Participants included radiologists from academic and community radiology practices, informatics leaders responsible for AI implementation, regulatory agency employees, and specialty society representatives. The major themes that emerged fell into two categories: (1) AI product development and (2) implementation of AI-based applications in clinical practice. In particular, participants highlighted key aspects of AI product development to include clear clinical task definitions; well-curated data from diverse geographic, economic, and health care settings; standards and mechanisms to monitor model reliability; and transparency regarding model performance, both in controlled and real-world settings. For implementation, participants emphasized the need for strong institutional governance; systematic evaluation, selection, and validation methods conducted by local teams; seamless integration into the clinical workflow; performance monitoring and support by local teams; performance monitoring by external entities; and alignment of incentives through credentialing and reimbursement. Participants predicted that clinical implementation of AI in radiology will continue to be limited until the safety, effectiveness, reliability, and transparency of such tools are more fully addressed.
    Language English
    Publishing date 2024-02-13
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2274861-1
    ISSN 1558-349X ; 1546-1440
    ISSN (online) 1558-349X
    ISSN 1546-1440
    DOI 10.1016/j.jacr.2024.01.024
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  6. Article ; Online: General-Purpose Large Language Models Versus a Domain-Specific Natural Language Processing Tool for Label Extraction From Chest Radiograph Reports.

    Savage, Cody H / Park, Hyoungsun / Kwak, Kijung / Smith, Andrew D / Rothenberg, Steven A / Parekh, Vishwa S / Doo, Florence X / Yi, Paul H

    AJR. American journal of roentgenology

    2024  Volume 222, Issue 4, Page(s) e2330573

    MeSH term(s) Natural Language Processing ; Humans ; Radiography, Thoracic/methods ; Radiology Information Systems
    Language English
    Publishing date 2024-01-17
    Publishing country United States
    Document type Letter ; Research Support, N.I.H., Extramural ; Journal Article
    ZDB-ID 82076-3
    ISSN 1546-3141 ; 0361-803X ; 0092-5381
    ISSN (online) 1546-3141
    ISSN 0361-803X ; 0092-5381
    DOI 10.2214/AJR.23.30573
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  7. Article ; Online: Developing a Comprehensive Resident-driven Research Training Pathway: A Chief Resident's Perspective.

    Lakhani, Dhairya A / Doo, Florence X / Chung, Charlotte

    Current problems in diagnostic radiology

    2022  Volume 52, Issue 2, Page(s) 93–96

    Abstract: Wide variation exists in research training, experience, opportunities, and exposure across various radiology residency training programs, ranging from having a dedicated research track to no exposure to hypothesis driven projects. Studies conducted at ... ...

    Abstract Wide variation exists in research training, experience, opportunities, and exposure across various radiology residency training programs, ranging from having a dedicated research track to no exposure to hypothesis driven projects. Studies conducted at different residency training programs with varied resources and National Institutes of Health funding have shown that resident-driven research initiatives and mentorship programs have the potential to improve research experience during residency training, engage more medical students in research, increase departmental peer-reviewed publications and increase peer-reviewed publications of early-career faculty physicians. In an attempt to standardize the research training during radiology residency, we propose a standardized resident-led program which institutions may adapt, as well as resources that the American Alliance of Academic Chief Residents in Radiology (A
    MeSH term(s) Humans ; United States ; Surveys and Questionnaires ; Internship and Residency ; Education, Medical, Graduate ; Faculty ; Radiology/education
    Language English
    Publishing date 2022-07-30
    Publishing country United States
    Document type Journal Article
    ZDB-ID 198954-6
    ISSN 1535-6302 ; 0363-0188
    ISSN (online) 1535-6302
    ISSN 0363-0188
    DOI 10.1067/j.cpradiol.2022.07.004
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  8. Article ; Online: Planetary Health and Radiology: Why We Should Care and What We Can Do.

    McKee, Hayley / Brown, Maura J / Kim, Helen H R / Doo, Florence X / Panet, Hayley / Rockall, Andrea G / Omary, Reed A / Hanneman, Kate

    Radiology

    2024  Volume 311, Issue 1, Page(s) e240219

    Abstract: Climate change adversely affects the well-being of humans and the entire planet. A planetary health framework recognizes that sustaining a healthy planet is essential to achieving individual, community, and global health. Radiology contributes to the ... ...

    Abstract Climate change adversely affects the well-being of humans and the entire planet. A planetary health framework recognizes that sustaining a healthy planet is essential to achieving individual, community, and global health. Radiology contributes to the climate crisis by generating greenhouse gas (GHG) emissions during the production and use of medical imaging equipment and supplies. To promote planetary health, strategies that mitigate and adapt to climate change in radiology are needed. Mitigation strategies to reduce GHG emissions include switching to renewable energy sources, refurbishing rather than replacing imaging scanners, and powering down unused scanners. Radiology departments must also build resiliency to the now unavoidable impacts of the climate crisis. Adaptation strategies include education, upgrading building infrastructure, and developing departmental sustainability dashboards to track progress in achieving sustainability goals. Shifting practices to catalyze these necessary changes in radiology requires a coordinated approach. This includes partnering with key stakeholders, providing effective communication, and prioritizing high-impact interventions. This article reviews the intersection of planetary health and radiology. Its goals are to emphasize why we should care about sustainability, showcase actions we can take to mitigate our impact, and prepare us to adapt to the effects of climate change. © RSNA, 2024
    MeSH term(s) Humans ; Climate Change ; Global Health ; Greenhouse Gases ; Radiology ; Radiology Department, Hospital/organization & administration
    Chemical Substances Greenhouse Gases
    Language English
    Publishing date 2024-03-13
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 80324-8
    ISSN 1527-1315 ; 0033-8419
    ISSN (online) 1527-1315
    ISSN 0033-8419
    DOI 10.1148/radiol.240219
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  9. Article ; Online: Turning a Page in the Yellow Journal: Figure Legends and Gender-Inclusive Patient Descriptors.

    Doo, Florence X / Zavaletta, Vaz / Carroll, Evelyn F / Ellis, Keri L / Rosenkrantz, Andrew B

    AJR. American journal of roentgenology

    2022  Volume 219, Issue 1, Page(s) 1–2

    Language English
    Publishing date 2022-04-13
    Publishing country United States
    Document type Editorial
    ZDB-ID 82076-3
    ISSN 1546-3141 ; 0361-803X ; 0092-5381
    ISSN (online) 1546-3141
    ISSN 0361-803X ; 0092-5381
    DOI 10.2214/AJR.22.27536
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Repeat Annual Lung Cancer Screening After Baseline Screening Among Screen-Negative Individuals: No-Cost Coverage Is Not Enough.

    Tailor, Tina D / Bell, Sarah / Doo, Florence X / Carlos, Ruth C

    Journal of the American College of Radiology : JACR

    2022  Volume 20, Issue 1, Page(s) 29–36

    Abstract: Purpose: Adherence to lung cancer screening (LCS) is central to effective screening. The authors evaluated the likelihood of repeat annual LCS in a national commercially insured population and associations with individual characteristics, insurance ... ...

    Abstract Purpose: Adherence to lung cancer screening (LCS) is central to effective screening. The authors evaluated the likelihood of repeat annual LCS in a national commercially insured population and associations with individual characteristics, insurance characteristics, and annual out-of-pocket cost (OOPC) burden.
    Methods: Using claims data from an employer-insured population (Clinformatics), individuals 55 to 80 years of age undergoing LCS between January 1, 2015, to September 30, 2019, with "negative" LCS were included. Repeat LCS was defined as low-dose chest CT occurring 10 to 15 months after the preceding LCS. Analysis was conducted over a 6-year period. Multivariable logistic regression was used to evaluate associations between repeat LCS and individual characteristics, insurance characteristics, and total OOPC incurred by the individual in the year of the index LCS, even if unrelated to LCS.
    Results: Of 14,943 individuals with negative LCS, 4,561 (30.5%) underwent repeat LCS. Likelihood of repeat LCS was decreased for men (adjusted odds ratio [aOR], 0.91; 95% confidence interval [CI], 0.86-0.97), Hispanic ethnicity (aOR, 0.82; 95% CI, 0.69-0.97), and indemnity insurance plans (aOR, 0.36; 95% CI, 0.25-0.53). Relative to New England, individuals in nearly all US geographic regions were less likely to undergo repeat LCS. Finally, individuals with total OOPC in the highest two quartiles were less likely to undergo repeat LCS (aOR, 0.85 [95% CI, 0.77-0.92] for OOPC >$1,069.02-$2,475.09 vs $0-$351.82; aOR, 0.75 [95% CI, 0.68-0.82] for OOPC >$2,475.09 vs $0-$351.82).
    Conclusions: Although federal policies facilitate LCS without cost sharing, individuals incurring high OOPC, even when unrelated to LCS, are less likely to undergo repeat LCS. Future policy design should consider the permeative burden of OOPC across the health continuum on preventive services use.
    MeSH term(s) Male ; Humans ; Lung Neoplasms/diagnostic imaging ; Early Detection of Cancer ; Preventive Health Services ; Odds Ratio ; Mass Screening
    Chemical Substances oleyloxyethylphosphorylcholine (96720-06-8)
    Language English
    Publishing date 2022-11-25
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2274861-1
    ISSN 1558-349X ; 1546-1440
    ISSN (online) 1558-349X
    ISSN 1546-1440
    DOI 10.1016/j.jacr.2022.11.005
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