Radiology-Artificial Intelligence

Scope & Guideline

Pioneering the Future of Radiology with AI Insights

Introduction

Explore the comprehensive scope of Radiology-Artificial Intelligence through our detailed guidelines, including its aims and scope. Stay updated with trending and emerging topics, and delve into declining areas to understand shifts in academic interest. Our guidelines also showcase highly cited topics, featuring influential research making a significant impact. Additionally, discover the latest published papers and those with high citation counts, offering a snapshot of current scholarly conversations. Use these guidelines to explore Radiology-Artificial Intelligence in depth and align your research initiatives with current academic trends.
LanguageEnglish
ISSN2638-6100
PublisherRADIOLOGICAL SOC NORTH AMERICA (RSNA)
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 2019 to 2024
AbbreviationRADIOL-ARTIF INTELL / Radiology-Artificial Intelligence
Frequency-
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address820 JORIE BLVD, SUITE 200 , OAK BROOK, ILLINOIS 60523-2251, UNITED STATES

Aims and Scopes

The journal 'Radiology-Artificial Intelligence' focuses on the intersection of radiology and artificial intelligence, emphasizing innovative methodologies and applications that enhance diagnostic and predictive capabilities in medical imaging.
  1. Application of AI in Radiology:
    The journal emphasizes the integration of artificial intelligence techniques in radiology, covering various applications such as image interpretation, segmentation, and risk prediction across diverse imaging modalities.
  2. Deep Learning Methodologies:
    A consistent focus on deep learning algorithms, especially in the context of enhancing image analysis, automating detection tasks, and improving diagnostic accuracy in radiological assessments.
  3. Radiomics and Quantitative Imaging:
    Exploration of radiomics, particularly the extraction of quantitative features from medical images, to assess tumor characteristics and predict clinical outcomes.
  4. Interdisciplinary Approaches:
    Encouragement of interdisciplinary research that combines insights from radiology, computer science, and statistics to develop robust AI models for clinical applications.
  5. Ethical and Regulatory Considerations:
    Discussion of the ethical implications and regulatory challenges associated with implementing AI technologies in clinical radiology, aiming to ensure safe and effective use.
The journal has identified several emerging trends that are shaping the future of AI in radiology, reflecting advancements in technology and a growing understanding of AI's potential in clinical practice.
  1. Integration of AI with Clinical Workflow:
    Recent publications emphasize the integration of AI tools into clinical workflows, exploring how AI can assist radiologists in decision-making, thereby improving efficiency and diagnostic accuracy.
  2. AI for Predictive Analytics:
    There is a rising trend towards using AI for predictive analytics in radiology, particularly in predicting patient outcomes and disease progression based on imaging data.
  3. Ethics and Bias Mitigation in AI:
    An increasing number of studies are addressing the ethical considerations and potential biases in AI algorithms, reflecting a growing awareness of the need for fairness and accountability in AI applications.
  4. Collaborative AI Systems:
    Emerging research focuses on collaborative systems where AI and radiologists work together, enhancing diagnostic processes while minimizing the risk of errors and improving patient care.
  5. AI in Global Health:
    The journal is seeing a trend towards exploring AI applications in global health contexts, addressing disparities in healthcare access and the role of AI in improving diagnostic capabilities in underserved populations.

Declining or Waning

While the journal continues to explore a wide range of topics, some themes have shown a decline in focus, possibly reflecting shifts in research priorities and emerging technologies.
  1. Traditional Imaging Techniques:
    There is a noticeable decrease in emphasis on traditional imaging techniques without AI enhancement, as the field gravitates towards advanced AI-driven methodologies that offer greater diagnostic utility.
  2. Basic Image Processing Techniques:
    Papers focusing solely on basic image processing without incorporation of advanced AI methods are becoming less frequent, indicating a shift towards more sophisticated, AI-integrated approaches.
  3. General Reviews of AI in Radiology:
    The journal has moved away from broad reviews of AI applications in radiology, favoring more specific studies that demonstrate clear clinical applications and outcomes.
  4. Single-Modal AI Applications:
    There is a waning interest in AI applications that focus on single imaging modalities, as researchers increasingly seek multi-modal approaches that leverage data from various imaging sources for improved accuracy and insights.

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