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1MMRIT Scholar, Atal Bihari Vajpayee Medical University, Lucknow, Uttar-Pradesh 2Assistant Director, School of Health Sciences, Chhatrapati Shahu Ji Maharaj University, Kanpur, Uttar-Pradesh, India 208024
Background: Artificial intelligence (AI) is revolutionizing radiology by improving image analysis, enhancing diagnostic accuracy, and streamlining workflows. Deep learning algorithms, especially convolutional neural networks (CNNs), have shown better performance in lesion detection, classification, and quantification. Challenges to implementing AI in clinical practice include ethical issues, regulatory barriers, and the requirement for strong validation.Aim: The current uses, advantages, and shortcomings of AI in radiology are reviewed, specifically image interpretation, workflow enhancement, and clinical decision support. In addition to the above, this review considers ethical issues, regulations, and prospective directions in adopting AI in radiology.Material and Method: Literature search using PubMed, PMC, and other biomedical databases was performed. Research on AI applications in radiology, encompassing diagnostic accuracy, workflow efficiency, and ethical/legal hurdles, was reviewed. Case studies, clinical trials, and meta-analyses were included to determine real-world performance and barriers to adoption.Results: AI has demonstrated considerable potential to enhance diagnostic accuracy (e.g., false positives in mammography by as much as 83%) and workflow efficiency (e.g., reporting times in emergency chest X-rays by as much as 77%). Variation between AI models exists, as well as an impact of bias in training data on performance. Ethical issues, including algorithmic bias and patient privacy, are still unanswered. Regulatory environments (FDA, CE marking) are changing, but legal responsibility for AI-aided diagnoses remains primarily with radiologists.Conclusion: AI has tremendous potential to improve radiology by making it more efficient and diagnostic accurate. Successful implementation, however, needs to overcome ethical, legal, and technical hurdles. Future developments must focus on explainable AI, standardized validation, and multidisciplinary collaboration to provide equitable and effective deployment.
Artificial intelligence (AI) is revolutionizing radiology by streamlining and improving image analysis. Deep models – particularly convolutional neural networks (CNNs) – are capable of learning sophisticated imaging patterns, sometimes outperforming human capabilities in image recognition(1)(2). In real life, AI technologies have already "improved diagnostic accuracy and efficiency in the detection of abnormalities across imaging modalities" with automated feature extraction. For instance, a deep-learning algorithm was created to classify chest X-rays for 14 different pathologies in seconds(3). By speeding up repetitive tasks (e.g. nodular or fracture screening), AI holds the promise of enhancing throughput and consistency in high-volume radiology departments(4). Radiologists and industry alike see AI as an aide to the increasing imaging burden: neuroimaging, chest CT, MRI and other modalities are typical targets for AI solutions, particularly for high-impact conditions such as lung cancer, stroke and breast cancer(4). AI's pattern-recognition capabilities can be very helpful in these fields; for example, detecting minute lesions or estimating the size of tumors, which could enhance early detection and patient-specific care(5). But incorporating AI in clinical practice introduces challenges (such as ensuring model validity and patient trust) that need to be confronted if its advantages are to be fully realized(6).
METHODOLOGY
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Shailendra Kumar
Corresponding author
MMRIT Scholar, Atal Bihari Vajpayee Medical University, Lucknow, Uttar-Pradesh
Shailendra Kumar* 1
Dheeraj Kumar
10.5281/zenodo.15400933