Title

Artificial intelligence improves radiologist workflow and assessment of image quality accuracy

Authors

Rishabh Yadav¹, Mukesh Kumar², Ifath Nazia Ghori³, Abhijeet Alok⁴, Amrita Pandita Bhatia^5,*, Santosh Kumar Kotnoor⁶ & Rahul Tiwari⁷

Affiliation

¹Department of Radiology, Prasad Medical College, Lucknow, Uttar Pradesh, India; ²Department of Oral & Maxillofacial Surgery, Shree Bankey Bihari Dental College, Masuri, Ghaziabad, Uttar Pradesh, India; ³Department of Computer Science, Jazan University, Jazan, Saudi Arabia; ⁴Department of Oral Medicine and Radiology, Sarjug Dental College and Hospital, Darbhanga, Bihar, India; ⁵Department of Prosthodontics (Crown and Bridges and Implantology), YCCM and RDF’s Dental College and Hospital, Ahilyanagar, Maharashtra, India; ⁶Department of Oral Pathology and Microbiology, HKES S. Nijalingappa Institute of Dental Science and Research, Kalaburgi, Karnataka, India; ⁷Department of Oral and Maxillofacial Surgery, Narsinhbhai Patel Dental College and Hospital, Sankalchand Patel University, Visnagar, Gujarat, India; *Corresponding author

Email

Rishabh Yadav - E-mail: drrishabh.always@gmail.com

Mukesh Kumar - E-mail: kumardrmukesh@gmail.com

 Ifath Nazia Ghori - E-mail: ighori@jazanu.edu.sa

 Abhijeet Alok - E-mail: drabhijeetalok786@gmail.com
Amrita Pandita Bhatia - E-mail: pandita.amrita@gmail.com

 Santosh Kumar Kotnoor - E-mail: santoshvevaan@gmail.com

Rahul Tiwari - E-mail: rtcfsurgeon@gmail.com

Article Type

Research Article

Date

Received March 1, 2026; Revised March 31, 2026; Accepted March 31, 2026, Published March 31, 2026

Abstract

Increasing imaging volumes and workflow pressures make consistent image quality assessment (IQA) challenging for radiologists in routine clinical practice. Artificial intelligence (AI) systems have recently been introduced to support quality control tasks, yet real-world evidence on their impact on radiologist workflow and diagnostic performance remains limited. Therefore, it is of interest to assess the effect of AI-IQA module of PACS/worklist on radiological workflow and accuracy in identifying suboptimal CT/MRI tests. Workflow (IQA time, TTFR and TAT) and IQA performance (sensitivity, specificity, kappa) metrics were evaluated in two groups (before and after 8 weeks of AI integration) and compared to an expert reference standard. AI integration lowered median IQA task time/examination and improved report turnaround time and improved radiologist sensitivity to suboptimal IQA without affecting specificity. Thus, we show AI-assisted IQA as a scalable tool to enhance the quality control and operation performance in the routine clinical practice.

Keywords

Artificial intelligence (AI); radiology workflow; image quality assessment (IQA); picture archiving and communication system (PACS) integration; turnaround time

Citation

Yadav et al. Bioinformation 22(3): 1322-1325 (2026)

Edited by

Vini Mehta

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.