stdClass Object
(
    [id] => 18386
    [paper_index] => 202512-01-025300
    [title] => AI-BASED AUTOMATED DISEASE DETECTION IN RADIOLOGY: CURRENT CAPABILITIES, CHALLENGES, AND FUTURE DIRECTIONS
    [description] => 
    [author] => Prateek Yalawar
    [googlescholar] => 
    [doi] => 
    [year] => 2025
    [month] => December
    [volume] => 11
    [issue] => 12
    [file] => fm/jpanel/upload/2025/December/202512-01-025300.pdf
    [abstract] => Artificial intelligence (AI) has rapidly emerged as a transformative technology in radiology, offering automated solutions for detecting a wide range of diseases across imaging modalities such as X-ray, CT, MRI, ultrasound, and mammography. Modern deep learning models frequently achieve radiologist-level performance in identifying abnormalities including pneumonia, lung nodules, breast cancer, intracranial hemorrhage, and musculoskeletal injuries. These AI systems improve diagnostic accuracy, speed up workflow, and act as reliable decision-support tools. However, several challenges limit their widespread adoption, including issues of algorithm bias, data heterogeneity, poor generalizability, lack of interpretability, and medico-legal concerns. Integration into clinical workflows, regulatory approvals, and real-world validation remain major barriers. This review summarizes current capabilities of AI-based disease detection in radiology, highlights existing challenges, and outlines future directions such as explainable AI, federated learning, multimodal imaging analytics, and human-AI collaborative practice. Understanding these aspects is crucial for safe, ethical, and effective deployment of AI in modern radiological practice.
    [keywords] => Artificial Intelligence, Radiology, Disease Detection, Deep Learning, Radiomics, Explainable Ai, Automated Diagnosis, Medical Imaging.
    [doj] => 2025-12-13
    [hit] => 
    [status] => 
    [award_status] => P
    [orderr] => 46
    [journal_id] => 1
    [googlesearch_link] => 
    [edit_on] => 
    [is_status] => 1
    [journalname] => EPRA International Journal of Multidisciplinary Research (IJMR)
    [short_code] => IJMR
    [eissn] =>  2455-3662 (Online)
    [pissn] => - --
    [home_page_wrapper] => images/products_image/11.IJMR.png
)
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