1Associate Professor, Department of Radiology, Galgotias University, Greater Noida, Uttar Pradesh, India 203201. ORCID: 0000-0002-0851-7368
2Assistant Professor, Centre of Vocational Studies, Islamic University of Science and Technology, J&K, India 192122. ORCID: 0009-0003-2871-2704
3,4Research Scholar, Department of Radio-Imaging Technology, Faculty of Allied Health Sciences, SGT University, Gurugram, Haryana 122505
5Assistant Professor, Department of Radio-Imaging Technology, Faculty of Allied Health Sciences, SGT University, Gurugram, Haryana 122505. ORCID: 0000-0002-2092-5313
6Assistant Professor, Department of Allied Health Sciences, Guru Jambheshwar University of Science & Technology, Hisar, Haryana, India 125001. ORCID: 0009-0007-5648-0460
*Corresponding Author: Mirza Burhan Saleem, Assistant Professor, Centre of Vocational Studies, Islamic University of Science and Technology, J&K, India
Background: Artificial Intelligence (AI) is rapidly transforming the field of radiology by improving diagnostic accuracy, enhancing image analysis, and optimizing clinical workflows. AI-based tools, such as machine learning algorithms and deep learning models, are increasingly incorporated into medical imaging systems. Despite these technological advancements, the successful integration of AI into routine clinical practice largely depends on radiologists' awareness, acceptance, and readiness to adopt these technologies. Understanding radiologists' perceptions and identifying barriers to AI implementation are, therefore, essential for the effective integration of AI into radiology practice.
Aim: To assess radiologists' perspectives on the integration of artificial intelligence in clinical radiology practice, including their level of awareness, attitudes toward AI, perceived barriers to adoption, and training needs.
Methodology: A mixed-methods research design was employed, combining quantitative and qualitative approaches. An online survey was distributed to practicing radiologists to collect data regarding their knowledge, perceptions, and experiences with AI technologies in radiology. The survey included questions on awareness of AI tools, perceived benefits, concerns about AI adoption, and institutional support. Additionally, semi-structured interviews were conducted with a subset of participants to obtain deeper insights into their experiences, concerns, and recommendations for improving AI integration in clinical settings. Quantitative data were analyzed using descriptive statistical methods, while qualitative responses were evaluated through thematic analysis.
Results: The study revealed that only 45% of radiologists were aware of AI tools currently used in clinical practice, indicating a significant knowledge gap. However, approximately 70% of respondents expressed a positive attitude toward AI, particularly acknowledging its potential to improve diagnostic accuracy and workflow efficiency. Major barriers identified included lack of training (65%), insufficient institutional support (55%), and concerns about AI reliability (50%). Additionally, 40% of radiologists expressed concerns regarding job security due to AI integration. A large majority (80% of respondents) emphasized the need for structured training programs and practical educational initiatives to improve AI literacy and confidence among radiologists.
Conclusion: Radiologists generally recognize the potential benefits of artificial intelligence in enhancing diagnostic performance and improving workflow efficiency in radiology. However, significant challenges, including limited awareness, insufficient training, and a lack of institutional support, hinder its widespread adoption. Addressing these barriers through targeted educational programs, increased institutional support, and interdisciplinary collaboration between radiologists and AI developers is essential for successful AI integration. AI should be implemented as a supportive tool that complements radiologists' expertise rather than replacing it, thereby improving overall patient care.
Keywords: Artificial Intelligence, Radiology, Medical Imaging, AI Adoption, Radiologists' Perception, Clinical Workflow, Diagnostic Accuracy.
How to cite this article: Singh DK, Saleem MB, Kumar R, Gautam A, Kant P, Bansal R, Assessing Radiologists' Perspectives on AI Integration in Clinical Practice. .Int J Drug Deliv Technol. 2026; 16(11s): 844-855; DOI: 10.25258/ijddt.16.11s.85
Source of support: Nil.
Conflict of interest: None