ISSN (Print): Awaited ISSN (Online): 3107-5932
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Short Communication

Artificial Intelligence in Digital Radiography: Current Applications and Future Trends

THE RADPAC | Volume 1, Issue 2 | September 2025
Authors: Souvid Modak, Amit Das
Received: Jun 10, 2025 Accepted: Aug 09, 2025 Published: Sep 20, 2025
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Abstract

Digital radiography (DR) remains the most widely performed imaging modality worldwide, forming the backbone of diagnostic workflows in emergency care, outpatient clinics, and screening programs. Despite technological advances, challenges such as image noise at low radiation doses, positioning errors, workflow inefficiencies, and increasing radiologist workload persist. Artificial intelligence (AI) has emerged as a transformative tool capable of addressing these limitations. This paper reviews the current applications of AI in digital radiography, including image enhancement, automated positioning assistance, abnormality detection, workflow optimization, and radiation dose reduction. Additionally, emerging trends such as multimodal AI integration, explainable AI, edge computing, federated learning, and ethical AI frameworks are discussed. The evidence suggests that AI-enhanced DR can improve diagnostic accuracy, reduce repeat examinations, optimize radiation dose, and support radiographers and radiologists in high-volume clinical environments. However, challenges related to validation, cost, bias, and regulatory standardization remain. Continued collaboration between clinicians, engineers, and regulatory bodies is essential to ensure the safe and equitable integration of AI into routine radiographic practice.

Keywords: Artificial intelligence, digital radiography, image enhancement, radiation dose optimization,

Article Information
DOI: 10.62502/tr/v1i2art1
Journal: THE RADPAC
Abbreviation: THE RADPAC
ISSN (Print): Awaited
ISSN (Online): 3107-5932
Volume/Issue: 1(2)
Pages: 1-3
References
  1. Kahn CE Jr. Radiology workflow and the need for innovation. Radiographics. 2020;40(3):629–640.
  2. Smith-Bindman R, Miglioretti DL, Johnson E, Lee C, Feigelson HS, Flynn M, et al. Use of diagnostic imaging studies and associated radiation exposure for patients enrolled in large integrated health care systems, 1996–2016. JAMA. 2021;325(15):1467–1479.
  3. Harolds JA, Parikh JR, Bluth EI, Dutton SC, Recht MP. Burnout of radiologists: Frequency, risk factors, and remedies. J Am Coll Radiol. 2021;18(3):445–451.
  4. Reiner BI. Workflow optimization in radiology: A systems approach. AJR Am J Roentgenol. 2020;215(3):505–511.
  5. Pesapane F, Codari M, Sardanelli F. Artificial intelligence in medical imaging: Threat or opportunity? Eur Radiol. 2022;32:1261–1273.
  6. Thrall JH. Workflow challenges in imaging. Radiology. 2021;299(2):245–252.
  7. Brady AP. Measuring workload in radiology: A review of methods and applications. Insights Imaging. 2020;11:23.
  8. European Society of Radiology (ESR). Radiology workforce shortages in Europe: Report from the ESR. Eur Radiol. 2022;32:1234–1242.
  9. Waite S, Scott J, Gale B, Fuchs T, Reede DL. Interpretive error in radiology. Radiology. 2020;295(2):290–298.
  10. Hanna TN, Zygmont ME, Peterson R, Theriot D, Shekhani H, Johnson JO. The effects of fatigue from overnight shifts on radiology error rates. AJR Am J Roentgenol. 2021;216(4):907–913.
  11. Reiner BI, Siegel EL, Carrino JA. Workflow optimization: Current challenges and future directions. J Digit Imaging. 2020;33:123–131.
  12. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2021;73:102173.
  13. Langlotz CP. Will artificial intelligence replace radiologists? Radiology. 2022;304(3):499–506.
  14. Lev MH, Demchuk AM, Johnston KC, Merino JG. Artificial intelligence in acute stroke imaging. Stroke. 2021;52:300–308.
  15. Maier-Hein L, Eisenmann M, Reinke A, Onogur S, Isensee F, Adler TJ, et al. Why rankings of biomedical image analysis competitions should be interpreted with care. Nat Med. 2020;26:144–152.
  16. Lakhani P, Sundaram B. Deep learning at chest radiography: Automated classification of pulmonary tuberculosis. Radiology. 2020;294(3):574–585.
  17. McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577:89–94.
  18. Pons E, Braun LM, Hunink MG, Kors JA. Natural language processing in radiology: A systematic review. J Digit Imaging. 2021;34:819–830.
  19. Larson DB, Towbin AJ, Pryor RM, Donnelly LF. Improving consistency in radiology reporting through structured reporting. Radiographics. 2020;40:395–405.
  20. Samek W, Montavon G, Vedaldi A, Hansen LK, Müller KR. Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. Nat Mach Intell. 2021;3:519–530.
How to Cite
Vancouver Style:
Modak S, Das A. Artificial Intelligence in Digital Radiography: Current Applications and Future Trends. THE RADPAC 2025;1(2):1-3. doi: 10.62502/tr/v1i2art1
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Article Details

Article Type:
Short Communication

Volume/Issue:
1(2)

Year:
2025

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