Title

Artificial intelligence in radiology and diagnostic imaging

Authors

Devta Viswam¹, Ashish Abraham Francis², Sachin Dawale³, Ranjani Kanakaraj⁴, Yagvalkya Sharma⁵, Lalit Karki^6,*, Shrinidhi Sivasubramaniam⁷ & A Prashannalakshmi⁸

Affiliation

¹Department of Radiology, Caritas Hospital, Kottayam, India; ²Institute of Internal Medicine, Madras Medical College, Chennai, India; ³Department of Radiodiagnosis, Mahatma Gandhi Institute of Medical Science, Sevagram, Wardha, Maharashtra, India; ⁴General Practitioner, Institute of Internal Medicine, Madras Medical College, Chennai, Tamil Nadu, India; ⁵Department of Research, Kalp Research Work, Mathura, Uttar Pradesh, India; ⁶Department of Radiology, Kathmandu Medical College, Kathmandu, Nepal India; ⁷Institute of Internal Medicine, Madras Medical College, Chennai, India; ⁸Department of Medicine, Tashkent Medical Academy, Urgench branch, Uzbekistan; *Corresponding author

Email

Devta Viswam - E-mail: devtaviswam@gmail.com; Phone: +91 8903693002
Ashish Abraham Francis - E-mail: ashishabfran@gmail.com; Phone: +91 9445504822
Sachin Dawale - E-mail: sachindawale97@gmail.com; Phone: +91 9545216294
Ranjani Kanakaraj - E-mail: ranjani16kanakaraj@gmail.com; Phone: +91 8754464163

Yagvalkya Sharma - E-mail: sonusharma23@gmail.com; Phone: +91 9953329363
Lalit Karki - E-mail: lalitjungkarki@gmail.com; Phone: +977-9841199101

Shrinidhi Sivasubramaniam - E-mail: shrinidhisiva2020@gmail.com; Phone: +91 9363100725 A Prashannalakshmi - E-mail: prashannahu@gmail.com; Phone: +91 9940594556

Article Type

Review

Date

Received July 1, 2025; Revised July 31, 2025; Accepted July 31, 2025, Published July 31, 2025

Abstract

Artificial intelligence applications in radiology and diagnostic imaging encompass machine learning algorithms and deep neural networks that enhance image acquisition, analysis, and interpretation. Automated detection tools improve lesion conspicuity and quantification in modalities such as computed tomography, magnetic resonance imaging, and ultrasound, yielding greater diagnostic accuracy and consistency. AI‑driven workflow optimization streamlines image reconstruction and prioritizes critical findings to accelerate clinical decision making and reduce reporting turnaround times. Integration of radiomics and predictive modeling facilitates noninvasive pheno-typing of tissue characteristics and risk stratification for personalized patient management. Despite promising outcomes, challenges remain in algorithm generalizability, integration with picture archiving and communication systems, and regulatory approval pathways. Ethical considerations including data privacy, algorithmic bias, and explainability must be addressed to ensure safe and equitable deployment. This narrative review synthesizes current developments, evaluates clinical efficacy, and outlines future directions for AI in radiology and diagnostic imaging.

Keywords

Artificial intelligence, machine learning, deep learning, radiomics, computer‑aided detection, workflow optimization, diagnostic imaging

Citation

Viswam et al. Bioinformation 21(7): 1891-1894 (2025)

Edited by

A Prashanth

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.