Application of Artificial Intelligence in MRI Image Analysis for Radiological Diagnosis: A Systematic Review

Muhammad Hudzaifah Nasrullah

doi:10.37430/jen.v8i1.241

Application of Artificial Intelligence in MRI Image Analysis for Radiological Diagnosis: A Systematic Review

Muhammad Hudzaifah Nasrullah

Abstract

Purpose: This systematic review critically evaluates recent advances in AI applied to MRI image analysis for radiological diagnosis, emphasizing improvements in diagnostic accuracy and clinical utility.

Methodology: A systematic literature review (SLR) was conducted using PRISMA guidelines, employing a PICOC framework. A comprehensive search of the Scopus database was performed, and studies were selected based on strict inclusion/exclusion criteria through screening and synthesis.

Findings: The review found that AI techniques significantly enhance MRI diagnostic performance (e.g., better tumor detection) and streamline workflows by automating routine tasks. It also notes growing publication trends from 2020–2024 in this field, reflecting increasing global research interest.

Research Limitations: The review is limited by its reliance on a single database (Scopus) and a narrow publication window (2020–2024). Many included studies exhibit data biases and lack comprehensive external validation, which may affect generalizability.

Practical Implications: These results suggest that AI integration can improve clinical workflows. The authors emphasize the need for standardized protocols and multidisciplinary collaboration to ensure safe and effective implementation of AI in radiological practice.

Originality: This study provides an original contribution by systematically synthesizing the latest literature on AI applications in MRI diagnostics, offering a comprehensive overview of current methods and trends. It fills a gap by critically evaluating recent studies and outlining future research directions.

Keywords

Artificial Intelligence; MRI; Radiological Diagnosis; Systematic Review; Diagnostic Accuracy; Clinical Efficiency.

Full Text:

PDF

References

G. Gravante et al., “Artificial intelligence and MRI in sinonasal tumors discrimination: where do we stand?,” Eur. Arch. Otorhinolaryngol., vol. 282, no. 3, pp. 1557–1566, 2025, doi: 10.1007/s00405-024-09169-9.

J. Jahn, J. Weiß, F. Bamberg, and E. Kotter, “Applications of artificial intelligence in radiology,” Radiologie, vol. 64, no. 10, pp. 752–757, 2024, doi: 10.1007/s00117-024-01357-2.

M. Codari, S. Schiaffino, F. Sardanelli, and R. M. Trimboli, “Artificial intelligence for breast MRI in 2008-2018: A systematic mapping review,” Am. J. Roentgenol., vol. 212, no. 2, pp. 280–292, 2019, doi: 10.2214/AJR.18.20389.

B. S. Kelly et al., “Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE),” Eur. Radiol., vol. 32, no. 11, pp. 7998–8007, 2022, doi: 10.1007/s00330-022-08784-6.

F. Gunzer, M. Jantscher, E. M. Hassler, T. Kau, and G. Reishofer, “Reproducibility of artificial intelligence models in computed tomography of the head: a quantitative analysis,” Insights Imaging, vol. 13, no. 1, 2022, doi: 10.1186/s13244-022-01311-7.

Y. Tian, T. E. Komolafe, and W. Zhang, “AI APPLICATIONS IN DIAGNOSTICS AND TREATMENT,” in Modern Technologies in Healthcare: AI, Computer Vision, Robotics, 2025, pp. 56–77. doi: 10.1201/9781003481959-4.

Y. A. Vasiliev et al., “Review of meta-analyses on the use of artificial intelligence in radiology,” Med. Vis., vol. 28, no. 3, pp. 22–41, 2024, doi: 10.24835/1607-0763-1425.

M. Sollini, L. Antunovic, A. Chiti, and M. Kirienko, “Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics,” Eur. J. Nucl. Med. Mol. Imaging, vol. 46, no. 13, pp. 2656–2672, 2019, doi: 10.1007/s00259-019-04372-x.

M. Fundoni, L. Porcu, and G. Melis, “Systematic literature review: Main procedures and guidelines for interpreting the results,” in Researching and Analysing Business: Research Methods in Practice, 2023, pp. 55–74. doi: 10.4324/9781003107774-5.

M. Višić, “CONNECTING PUZZLE PIECES: SYSTEMATIC LITERATURE REVIEW METHOD IN THE SOCIAL SCIENCES,” Sociologija, vol. 64, no. 4, p. 543, 2022, doi: 10.2298/SOC2204543V.

M. I. Riaño-Casallas and S. Rojas-Berrio, “How to Report Systematic Literature Reviews in Management Using SyReMa,” Innovar, vol. 34, no. 92, 2023, doi: 10.15446/innovar.v34n92.99156.

R. van Dinter, C. Catal, and B. Tekinerdogan, “A Multi-Channel Convolutional Neural Network approach to automate the citation screening process,” Appl. Soft Comput., vol. 112, 2021, doi: 10.1016/j.asoc.2021.107765.

F. G. Aleu and H. Keathley, “Design and application of a meta-evaluation framework,” presented at the IIE Annual Conference and Expo 2015, 2015, pp. 1777–1786. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84971009513&partnerID=40&md5=86f28f4cd5da29cc583019c4d74fca45

K. Harry and M. Alrezq, “Assessment of Critical Success Factors Using Meta-synthesis Evaluation,” presented at the IISE Annual Conference and Expo 2022, 2022. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137172797&partnerID=40&md5=142e9a05d15585cd8118051d0379fba3

L. M. Rodríguez-Carmona and P. Yustres Duro, “Keys to Usability in Retail E-Commerce: A Systematic Review of the Literature,” UCJC Bus. Soc. Rev., vol. 21, no. 80, pp. 778–815, 2024, doi: 10.3232/UBR.2024.V21.N1.17.

F. J. García-Peñalvo, “Developing robust state-of-the-art reports: Systematic Literature Reviews,” Educ. Knowl. Soc., vol. 23, p. E28600, 2022, doi: 10.14201/eks.28600.

K. L. Lane and R. J. Kettler, “Literature Review, Questions, and Hypotheses,” in Research Methodologies of School Psychology: Critical Skills, 2019, pp. 24–41. doi: 10.4324/9781315724072-2.

M. Saputra, P. I. Santosa, and A. E. Permanasari, “Consumer Behaviour and Acceptance in Fintech Adoption: A Systematic Literature Review,” Acta Inform. Pragensia, vol. 12, no. 2, pp. 468–489, 2023, doi: 10.18267/j.aip.222.

V. O. Trung Quang and A. Riewpaiboon, “A literature review of health economic evaluation: A case of vaccination on systematic review analysis,” Int. J. Pharm. Sci. Rev. Res., vol. 39, no. 2, pp. 300–308, 2016.

A. Landschaft et al., “Implementation and evaluation of an additional GPT-4-based reviewer in PRISMA-based medical systematic literature reviews,” Int. J. Med. Inf., vol. 189, 2024, doi: 10.1016/j.ijmedinf.2024.105531.

V. Mishra and M. P. Mishra, “PRISMA FOR REVIEW OF MANAGEMENT LITERATURE – METHOD, MERITS, AND LIMITATIONS – AN ACADEMIC REVIEW,” Rev. Manag. Lit., vol. 2, pp. 125–136, 2023, doi: 10.1108/S2754-586520230000002007.

D. Moher, D. G. Altman, and J. Tetzlaff, “PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses),” in Guidelines for Reporting Health Research: A User’s Manual, 2014, pp. 250–261. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178329658&partnerID=40&md5=9f5d0e12fab5decab7d997e7451124e9

L. Feng, D. Ma, and F. Liu, “Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends,” NMR Biomed., vol. 35, no. 4, 2022, doi: 10.1002/nbm.4416.

K. Wenderott, J. Krups, J. A. Luetkens, N. Gambashidze, and M. Weigl, “Prospective effects of an artificial intelligence-based computer-aided detection system for prostate imaging on routine workflow and radiologists’ outcomes,” Eur. J. Radiol., vol. 170, 2024, doi: 10.1016/j.ejrad.2023.111252.

D. Corradini et al., “Challenges in the use of artificial intelligence for prostate cancer diagnosis from multiparametric imaging data,” Cancers, vol. 13, no. 16, 2021, doi: 10.3390/cancers13163944.

E. Dikici, M. Bigelow, L. M. Prevedello, R. D. White, and B. S. Erdal, “Integrating AI into radiology workflow: Levels of research, production, and feedback maturity,” J. Med. Imaging, vol. 7, no. 1, 2020, doi: 10.1117/1.JMI.7.1.016502.

N. C. Swinburne et al., “Semisupervised Training of a Brain MRI Tumor Detection Model Using Mined Annotations,” Radiology, vol. 303, no. 1, pp. 80–89, 2022, doi: 10.1148/RADIOL.210817.

Y. P. Ongena, M. Haan, D. Yakar, and T. C. Kwee, “Patients’ views on the implementation of artificial intelligence in radiology: development and validation of a standardized questionnaire,” Eur. Radiol., vol. 30, no. 2, pp. 1033–1040, 2020, doi: 10.1007/s00330-019-06486-0.

Y. Toyohara, K. Sone, K. Noda, K. Yoshida, S. Kato, M. Kaiume, A. Taguchi, R. Kurokawa, dan Y. Osuga, "The automatic diagnosis artificial intelligence system for preoperative magnetic resonance imaging of uterine sarcoma," J. Gynecol. Oncol., vol. 35, no. 3, p. e24, Mei 2024, doi: 10.3802/jgo.2024.35.e24.

N. C. Swinburne et al., "Semisupervised Training of a Brain MRI Tumor Detection Model Using Mined Annotations," Radiology, vol. 303, no. 1, pp. 80–89, Apr. 2022, doi: 10.1148/radiol.210817.

K. Wenderott, J. Krups, J. A. Luetkens, N. Gambashidze, dan M. Weigl, "Prospective effects of an artificial intelligence-based computer-aided detection system for prostate imaging on routine workflow and radiologists' outcomes," Eur. J. Radiol., vol. 170, p. 111252, Jan. 2024, doi: 10.1016/j.ejrad.2023.111252.

D. Corradini et al., "Challenges in the Use of Artificial Intelligence for Prostate Cancer Diagnosis from Multiparametric Imaging Data," Cancers (Basel), vol. 13, no. 16, p. 3944, Aug. 2021, doi: 10.3390/cancers13163944.

R. Osuala et al., "medigan: a Python library of pretrained generative models for medical image synthesis," J. Med. Imaging (Bellingham), vol. 10, no. 6, p. 061403, Nov. 2023, doi: 10.1117/1.JMI.10.6.061403.

D. M. Ravid, J. C. White, D. L. Tomczak, A. F. Miles, dan T. S. Behrend, "A meta‐analysis of the effects of electronic performance monitoring on work outcomes," Personnel Psychology, vol. 76, no. 1, pp. 5–40, Mar. 2023.

DOI: https://doi.org/10.37430/jen.v8i1.241