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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
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Emerging Medical Imaging Technologies and Educational Approaches.

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    Integrating artificial intelligence (AI) and informatics into medical imaging education is crucial. Challenges include curriculum limitations and faculty readiness, necessitating structured training for ethical AI adoption and patient care.

    Keywords:
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    Area of Science:

    • Medical Imaging Education
    • Artificial Intelligence
    • Health Informatics

    Background:

    • Emerging technologies like AI are transforming medical imaging practice.
    • There is a growing need to update medical imaging curricula to include AI and informatics.
    • Understanding the impact of AI on clinical decision-making is essential for radiographers.

    Purpose of the Study:

    • To review current literature on integrating AI and informatics into medical imaging education.
    • To identify key themes and trends in AI and informatics implementation in radiography programs.
    • To explore ethical considerations associated with AI adoption in medical imaging.

    Main Methods:

    • Systematic review of peer-reviewed literature from the past 5 years.
    • Focus on medical imaging education, radiography curricula, AI applications, and ethics.
    • Analysis of articles to identify recurring themes in AI and informatics integration.

    Main Results:

    • Four key themes emerged: AI integration in education, essential informatics concepts, clinical AI applications, and ethical considerations.
    • AI and informatics are increasingly vital for medical imaging professionals.
    • Ethical concerns include algorithmic bias and impact on professional judgment.

    Conclusions:

    • Integrating AI and informatics into medical imaging education is essential but faces challenges like curriculum constraints and faculty preparedness.
    • Responsible AI implementation requires addressing ethical concerns and developing standardized educational frameworks.
    • Prioritizing structured education and training is crucial for safe AI use in medical imaging, ensuring continued ethical and patient-centered care.