Exploring the Role of Artificial Intelligence in Radiodiagnostics and Radiotherapy: A Literature Review from the Indonesian Context
DOI:
https://doi.org/10.38035/ijam.v4i3.1390Keywords:
Artificial Intelligence, Radiodiagnostik, Radioterapi, Indonesia, Literatur ReviewAbstract
The utilization of artificial intelligence (AI) in the fields of radiodiagnostics and radiotherapy has been rapidly advancing worldwide, contributing significantly to improving diagnostic accuracy, work efficiency, and the personalization of cancer therapy. In Indonesia, this development has begun to gain attention through local research, national initiatives, and limited implementation in healthcare facilities. This article aims to review the literature on the use of AI in radiodiagnostics and radiotherapy in Indonesia, covering research trends, application areas, and implementation challenges. The literature search was conducted using international databases (PubMed, Scopus, Google Scholar) and national sources (Garuda, university repositories) with relevant keywords, publication years ranging from 2015 to 2025, and inclusion criteria focused on studies within the Indonesian context. The review findings indicate that AI has been utilized in various aspects of radiodiagnostics, such as mammography analysis, X-rays, and cervical cancer screening, while in radiotherapy, AI has been applied in auto-contouring, dose planning, and quality assurance. Although the potential of AI utilization is highly promising, the main challenges include limited local datasets, infrastructure readiness, regulations, and human resource competence. This article concludes that the development of AI in radiology and radiotherapy in Indonesia requires interdisciplinary collaboration, data standardization, policy support, and large-scale clinical validation studies to ensure safe, effective, and sustainable implementation.
References
Apriantoro, T., Kartika, R., & Kurniawan, B. (2023). Optimization of intensity modulated radiation therapy (IMRT) in post-mastectomy patients. Journal of Radiotherapy Advances, 15(2), 87–94.
Azhar Jaya. (2024). Implementasi teknologi kecerdasan buatan dalam layanan kesehatan nasional. Kementerian Kesehatan Republik Indonesia.
Badan Pengawas Tenaga Nuklir. (2022). Laporan pengawasan fasilitas radioterapi dan keamanan data medis di Indonesia. BAPETEN.
Bugani, D., Pulungan, A., & Suryana, M. (2024). Feasibility study of radiotherapy infrastructure in Indonesia: Challenges and future directions. Indonesian Journal of Medical Physics, 4(1), 55–63.
Darmiati, E., Pramudito, Y., & Arifin, R. (2023). Application of AI in mammography for breast cancer detection in Indonesia. Indonesian Journal of Biomedical Imaging, 9(1), 22–31.
Dewi, L., Santoso, A., & Permata, R. (2023). AI-based quality assurance in radiotherapy: A clinical feasibility study. Journal of Radiological Research, 10(3), 122–130.
Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., ... & Topol, E. (2021). Deep learning-enabled medical computer vision. Nature Medicine, 27(6), 1153–1161.
Fauzi, R., & Handayani, T. (2023). Integrasi ilmu kedokteran dan data science dalam pendidikan tinggi kesehatan di Indonesia. Journal of Health Informatics Education, 2(2), 45–56.
Hartono, B. (2020). Financial barriers to adopting AI software in regional hospitals. Indonesian Health Technology Review, 5(1), 14–21.
Hidayati, N., & Lestari, M. (2023). Bias model AI akibat keterbatasan dataset lokal dalam pencitraan radiologi Indonesia. Journal of Clinical Imaging, 8(4), 201–210.
Kementerian Kesehatan RI. (2022). Indonesia Health Tech Roadmap 2025. Jakarta: Kemenkes RI.
Komalasari, N. (2024). Machine learning validation in patient-specific radiotherapy planning. Indonesian Journal of Oncology Physics, 3(1), 11–19.
Kurniawan, D., & Putri, A. (2020). AI-based tuberculosis detection using X-ray imaging in Indonesian hospitals. Journal of Medical Imaging Technology, 5(2), 75–83.
Lee, J., Park, H., & Kim, S. (2022). Challenges of AI adoption in healthcare systems of developing countries. Health Informatics International, 7(1), 33–48.
Logan, P., Martin, G., & Chen, Y. (2023). FAIR data principles in medical AI model development. European Journal of Digital Health, 9(1), 56–67.
Nugroho, R., Fathurrahman, M., & Dewantara, I. (2021). AI-assisted auto-contouring for organ-at-risk delineation. Indonesian Journal of Radiotherapy, 6(2), 98–106.
Omoumi, P. (2024). Transparency and validation of AI models in radiology: Ethical and clinical perspectives. Radiology Ethics Journal, 12(1), 25–34.
Pratama, A., Susanti, R., & Wijaya, F. (2022). Convolutional neural networks for intracranial hemorrhage detection. Indonesian Journal of Neuroimaging, 4(3), 67–75.
Rahardjo, T., & Dewanti, S. (2022). Infrastructure disparity and AI adoption in Indonesian hospitals. Journal of Health Policy Studies, 8(2), 45–53.
Rahman, A., & Yuliani, E. (2023). International collaboration for AI advancement in radiotherapy. ASEAN Journal of Medical Technology, 9(1), 33–41.
Ramadhan, H., & Yuliani, E. (2024). Adaptive radiotherapy using AI: Early development and future direction in Indonesia. Journal of Radiation Oncology Science, 7(1), 21–28.
Santoso, H., Siregar, D., & Malik, F. (2021). National imaging database as a foundation for AI-based radiology research in Indonesia. Journal of Medical Informatics, 3(2), 54–63.
Sari, P., Utama, N., & Hapsari, A. (2021). Deep learning for breast cancer detection: Case study in Indonesia. Journal of Digital Radiology, 2(1), 18–27.
Setiawan, R. (2022). Cloud-based AI solutions for equitable healthcare in Indonesia. Journal of Health Informatics Research, 3(3), 44–53.
Susanto, L., & Wijaya, R. (2022). AI-driven dose personalization in cervical cancer radiotherapy. Journal of Oncology Informatics, 6(1), 59–68.
Utami, P., & Wibowo, S. (2023). AI-assisted ultrasound for early congenital anomaly detection. Indonesian Journal of Obstetric Imaging, 5(2), 39–47.
Wulandari, D., Putra, I., & Sitorus, J. (2023). Competency readiness of radiologists and radiotherapists in AI utilization. Journal of Medical Education and Practice, 11(1), 50–58
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