ARTIFICIAL INTELLIGENCE IN RADIOLOGY: SYSTEMATIC REVIEW

Authors

  • Firsty Tasya Evitasari Bayu Asih General Hospital, Purwakarta, Indonesia Author

DOI:

https://doi.org/10.61841/wasnx488

Keywords:

Artificial intelligent, machine learning, radiology

Abstract

Introduction: Radiology, integral to modern medicine since the discovery of X-rays, has evolved to integrate AI and machine learning, reshaping healthcare. This review explores their impact on radiology, aiming to guide discussions among clinicians, researchers, and policymakers for improved patient outcomes and future directions in AI-driven radiology.

Methods: The researchers in this study followed the 2020 Preferred Reporting Items for Systematic Review and MetaAnalysis (PRISMA) guidelines to ensure that their work met the required standards. This was done to ensure the precision and reliability of the conclusions derived from the research.

Result: Our search produced 17 results. After looking at the titles and summaries, we discovered 10 papers that fit our criteria after excluding several articles because they did not fit into criteria. But after reading the full papers carefully, we included five papers in our final analysis. These papers included a retrospective observational study and several case reports.

Conclusion: Overall, these studies and trials illustrate the promising potential of AI in healthcare, particularly in radiology, while underscoring the need for continued research, larger-scale studies, and addressing limitations to harness its full benefits and ensure patient-centered, effective, and ethical integration into medical practices.

References

Brady, A.P.; Bello, J.A.; Derchi, L.E.; Fuchsjäger, M.; Goergen, S.; Krestin, G.P.; Lee, E.J.Y.; Levin, D.C.; Pressacco, J.; Rao, V.M.; et al. Radiology in the era of value-based healthcare: A multi-society expert statement from the ACR, CAR, ESR, IS3R, RANZCR, and RSNA. Insights Imaging 2020, 11, 136.

Giardino, A.; Gupta, S.; Olson, E.; Sepulveda, K.; Lenchik, L.; Ivanidze, J.; Rakow-Penner, R.; Patel, M.J.; Subramaniam, R.M.; Ganeshan, D. Role of Imaging in the Era of Precision Medicine. Acad. Radiol. 2017, 24, 639– 649.

Hosny, A.; Parmar, C.; Quackenbush, J.; Schwartz, L.H.; Aerts, H.J. Artificial intelligence in radiology. Nat. Rev. Cancer 2018, 18, 500–510.

European Society of Radiology (ESR); European Federation of Radiographer Societies (EFRS). Patient Safety in Medical Imaging: A joint paper of the European Society of Radiology (ESR) and the European Federation of Radiographer Societies (EFRS). Insights Imaging 2019, 10, 45.

Dreyer, K.J.; Geis, J.R. When Machines Think: Radiology’s Next Frontier. Radiology 2017, 285, 713–718.

Bushberg, J.T.; Seibert, J.A.; Leidholdt, E.M. The Essential Physics of Medical Imaging, 4th ed.; Lippincott Williams & Wilkins (LWW): Philadelphia, PA, USA, 2020.

Cherry, S.R.; Jones, T.; Karp, J.S.; Qi, J.; Moses, W.W.; Badawi, R.D. Total-body PET: Maximizing sensitivity to create new opportunities for clinical research and patient care. J. Nucl. Med. 2018, 59, 3–12.

Uppot, R.; Laguna, B.; McCarthy, C.; De Novi, G.; Phelps, A.; Siegel, E.; Courtier, J. Implementing Virtual and Augmented Reality Tools for Radiology Education and Training, Communication, and Clinical Care. Radiology 2019, 291, 570–580.

von Ende, E.; Ryan, S.; Crain, M.; Makary, M. Artificial Intelligence, Augmented Reality, and Virtual Reality Advances and Applications in Interventional Radiology. Diagnostics 2023, 13, 892.

Barreiro-Ares, A.; Morales-Santiago, A.; Sendra-Portero, F.; Souto-Bayarri, M. Impact of the Rise of Artificial Intelligence in Radiology: What Do Students Think? Int. J. Environ. Res. Public Health 2023, 20,1589. https://doi.org/10.3390/ ijerph20021589

Muehlematter, U. J., Daniore, P., & Vokinger, K. N. (2021). Approval of artificial intelligence and machine learningbased medical devices in the USA and Europe (2015–20): a comparative analysis. The Lancet Digital Health, 3(3), e195–e203. doi:10.1016/s2589-7500(20)30292-2

Mayo, R.C., Kent, D., Sen, L.C. et al. Reduction of False-Positive Markings on Mammograms: a Retrospective Comparison Study Using an Artificial Intelligence-Based CAD. J Digit Imaging 32, 618–624 (2019). https://doi.org/10.1007/s10278-018-0168-6

R. Lee, D. Jarchi, R. Perera, A. Jones, I. Cassimjee, A. Handa, D.A. Clifton, K. Bellamkonda, F. Woodgate, N. Killough, N. Maistry, A. Chandrashekar, C.R. Darby, A. Halliday, L.J. Hands, P. Lintott, T.R. Magee, A. Northeast, J. Perkins, E. Sideso, Applied Machine Learning for the Prediction of Growth of Abdominal Aortic Aneurysm in Humans, EJVES Short Reports, Volume 39, 2018, Pages 24-28,ISSN 2405-6553, https://doi.org/10.1016/j.ejvssr.2018.03.004. (https://www.sciencedirect.com/science/article/pii/S2405655318300094)

Nam, J.G.; Hwang, E.J.; Kim, J.; Park, N.; Lee, E.H.; Kim, H.J.; Nam, M.; Lee, J.H.; Park, C.M.; Goo, J.M. AI Improves Nodule Detection on Chest Radiographs in a Health Screening Population: A Randomized Controlled Trial. Radiology 2023, 307, e221894.

Downloads

Published

2023-12-18

How to Cite

Evitasari, F. T. (2023). ARTIFICIAL INTELLIGENCE IN RADIOLOGY: SYSTEMATIC REVIEW. Journal of Advanced Research in Medical and Health Science (ISSN 2208-2425), 9(12), 115-124. https://doi.org/10.61841/wasnx488

Similar Articles

11-20 of 21

You may also start an advanced similarity search for this article.