Artificial Intelligence in Radiology: Transforming Diagnostics and Raising Ethical Dilemmas

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.