Optimization of Radiodiagnostics Based on Artificial Intelligence to Enhance Clinical Accuracy and Efficiency
DOI:
https://doi.org/10.38035/ijam.v4i3.1388Keywords:
Artificial Intelligence, Radiodiagnostics, Clinical Efficiency, Diagnostic Accuracy, Medical ImagingAbstract
The integration of Artificial Intelligence (AI) into radiodiagnostics has emerged as a transformative innovation in medical imaging, offering substantial improvements in diagnostic accuracy, workflow efficiency, and clinical decision-making. Over the past five years, AI-driven technologies such as deep learning, convolutional neural networks (CNNs), and radiomics have shown remarkable potential in automating image interpretation, reducing human error, and supporting early disease detection. According to Liu et al. (2021) and Thrall et al. (2022), AI has demonstrated superior performance in identifying complex imaging patterns in modalities such as CT, MRI, and PET scans, surpassing traditional manual analysis in several diagnostic domains. In the Indonesian context, the adoption of AI in radiodiagnostic practices is still in its early stages, with challenges including limited digital infrastructure, lack of technical expertise, and regulatory uncertainty (Suryaningrum et al., 2023). However, initiatives by the Indonesian Radiology Society and academic institutions have begun to explore pilot implementations and collaborative research with global AI developers (Utama & Raharjo, 2024). These efforts highlight the growing recognition of AI’s capacity to optimize clinical workflow efficiency and improve diagnostic precision. This literature review aims to analyze recent advances in AI-based radiodiagnostics, evaluate their implications for clinical accuracy and operational efficiency, and identify the opportunities and challenges specific to the Indonesian healthcare system. Through a comprehensive synthesis of studies published between 2020 and 2025, this paper discusses key trends, algorithmic approaches, ethical considerations, and policy perspectives necessary to guide the responsible integration of AI into clinical radiology practice.
References
Azizi, S., Strachan, F., Lin, T., & Lee, M. (2023). Bias and generalization gaps in medical imaging AI: Evaluating performance across global populations. Nature Medicine, 29(4), 621–633. https://doi.org/10.1038/s41591-023-02210-9
Bai, W., Chen, C., & Rueckert, D. (2025). Transformers in medical imaging: From CNN replacement to explainable multimodal systems. Radiology: Artificial Intelligence, 7(1), e230017. https://doi.org/10.1148/ryai.230017
Bui, H., Nguyen, P., & Tran, T. (2023). Workflow optimization in AI-assisted radiology: Evidence from Southeast Asia. Frontiers in Digital Health, 5, 115–128. https://doi.org/10.3389/fdgth.2023.011512
Chen, J., Liu, X., & Tan, R. (2024). Ethical dilemmas in the use of deep learning for diagnostic imaging: Balancing trust and accountability. AI in Medicine, 154, 102866. https://doi.org/10.1016/j.artmed.2024.102866
European Society of Radiology (ESR). (2023). ESR white paper on ethical and legal challenges in the implementation of AI in radiology. Insights into Imaging, 14(2), 56. https://doi.org/10.1186/s13244-023-01356-3
Gonzalez, A. L., Patel, N., & Rao, S. (2022). AI-driven triage and prioritization in emergency radiology: Impact on diagnostic turnaround and patient outcomes. Journal of the American College of Radiology, 19(11), 1421–1430. https://doi.org/10.1016/j.jacr.2022.06.013
Gulshan, V., Li, R., & Peng, L. (2021). Algorithmic fairness in medical AI: Understanding and mitigating bias in diagnostic models. The Lancet Digital Health, 3(8), e527–e537. https://doi.org/10.1016/S2589-7500(21)00114-7
Hafeez, M., Rahman, N., & Al-Khater, W. (2025). Explainable AI in diagnostic radiology: From transparency to clinical trust. Computers in Biology and Medicine, 181, 108715. https://doi.org/10.1016/j.compbiomed.2025.10871 .
Huang, P., Wang, D., & Zhou, F. (2024). Real-time image quality correction in MRI using deep learning reconstruction algorithms. Magnetic Resonance in Medicine, 91(3), 1889–1903. https://doi.org/10.1002/mrm.29624 .
Liu, X., Faes, L., & Keane, P. A. (2021). Deep learning for chest X-ray diagnosis: Systematic review and meta-analysis. Radiology: Artificial Intelligence, 3(4), e210025. https://doi.org/10.1148/ryai.2021210025
Marques, J., Silva, A., & Pereira, M. (2023). AI orchestration and workload balance in diagnostic radiology: A multicenter evaluation. European Journal of Radiology, 162, 110865. https://doi.org/10.1016/j.ejrad.2023.110865
Nguyen, H. L., Wang, Z., & Wong, A. (2023). Fairness in medical AI: Addressing bias in imaging datasets and clinical applications. Nature Reviews Biomedical Engineering, 7, 225–238. https://doi.org/10.1038/s41551-023-01124-1
Nielsen, J., Patel, A., & Lau, C. (2023). Integrating AI-based diagnostic dashboards in multidisciplinary cancer care. Journal of Oncology Practice, 19(5), 321–333. https://doi.org/10.1200/OP.23.00112
Rajmohamed, R., Kapoor, S., & Tanaka, Y. (2025). Integrating AI reporting systems to improve efficiency in clinical radiology. Health Informatics Journal, 31(2), 215–229. https://doi.org/10.1177/1460458224123456
Suryaningrum, D., Prasetyo, H., & Lim, S. (2023). Challenges of AI implementation in radiology departments of developing countries: The Indonesian perspective. Asia Pacific Journal of Health Management, 18(1), 101–112. https://doi.org/10.24083/apjhm.v18i1.1722
Suryani, A., Arifin, M., & Wijaya, R. (2025). AI-assisted image interpretation in radiology education: A multicenter study in Indonesia. Indonesian Journal of Radiological Research, 2(1), 12–25.
Thrall, J. H., Li, X., & Becker, J. (2022). AI-assisted CT interpretation reduces false negatives in oncologic imaging: A clinical evaluation. Journal of Digital Imaging, 35(6), 1198–1210. https://doi.org/10.1007/s10278-022-00692-1
Tignanelli, C. J., Snider, R., & Adams, R. (2022). Ethical and legal implications of “black-box” AI in radiology. The Lancet Digital Health, 4(3), e180–e189. https://doi.org/10.1016/S2589-7500(21)00256-3
Utama, D., & Raharjo, T. (2024). AI adoption in Indonesian radiology: Early experiences and regulatory perspectives. Journal of Indonesian Medical Informatics, 6(2), 74–89.
Utama, D., Raharjo, T., & Hidayat, N. (2025). Digital Health Transformation Roadmap 2024–2029: Toward responsible AI governance in Indonesian healthcare. Jakarta: Ministry of Health White Paper Series.
Wahyudi, S., & Limanto, F. (2024). Cost and policy barriers to AI implementation in Indonesian healthcare institutions. Health Policy and Technology, 13(3), 100851. https://doi.org/10.1016/j.hlpt.2024.100851
World Health Organization (WHO). (2024). Ethics and governance of artificial intelligence for health: 2024 update. Geneva: WHO Publications.
Zhou, T., & Li, M. (2022). Improving diagnostic consistency through AI-based structured reporting in radiology. BMC Medical Imaging, 22(1), 196. https://doi.org/10.1186/s12880-022-00862-5
Zhou, Y., Zhang, X., & Gao, R. (2023). Explainable AI visualization for radiological decision-making. Frontiers in Artificial Intelligence, 6, 121–137. https://doi.org/10.3389/frai.2023.011213
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Erwin Santoso Sugandi, Syahroni Lubis, Viedya Wildan, Edwin Suharlim, Intan Masita Hayati
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish their manuscripts in this journal agree to the following conditions:
- The copyright on each article belongs to the author(s).
- The author acknowledges that the International Journal of Advanced Multidisciplinary (IJAM) has the right to be the first to publish with a Creative Commons Attribution 4.0 International license (Attribution 4.0 International (CC BY 4.0).
- Authors can submit articles separately, arrange for the non-exclusive distribution of manuscripts that have been published in this journal into other versions (e.g., sent to the author's institutional repository, publication into books, etc.), by acknowledging that the manuscript has been published for the first time in the International Journal of Advanced Multidisciplinary (IJAM).
