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A Feasibility Study of Deep Learning-Based Auto-Segmentation Directly Used in VMAT Planning Design and Optimization for Cervical Cancer

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Intentional deep overfit learning (IDOL): A novel deep learning strategy for adaptive radiation therapy

Chun, J., Park, J. C., Olberg, S., Zhang, Y., Nguyen, D. T. D., Wang, J., Kim, J. S., & Jiang, S. B. (2021) · Medical Physics · doi:10.1002/mp.15352 · Cited by 4

Convergence of evolving artificial intelligence and machine learning techniques in precision oncology

Fountzilas, E., Pearce, T. E., Baysal, M. A., Chakraborty, A., & Tsimberidou, A. M. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01471-y · Cited by 233

A systematic review of the role of artificial intelligence in automating computed tomography-based adaptive radiotherapy for head and neck cancer

Mastella, E., Calderoni, F., Manco, L., Ferioli, M., Medoro, S., Turra, A., Giganti, M., & Stefanelli, A. (2025) · Physics and Imaging in Radiation Oncology · doi:10.1016/j.phro.2025.100731 · Cited by 31

Guidance on selecting and evaluating AI auto-segmentation systems in clinical radiotherapy: insights from a six-vendor analysis

Rusanov, B., Ebert, M. A., Sabet, M., Rowshanfarzad, P., Barry, N., Kendrick, J., Alkhatib, Z., Gill, S., Dass, J., Bucknell, N. W., et al. (2025) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-024-01513-x · Cited by 21

Integration of Artificial Intelligence-Enhanced Capsule Endoscopy in Clinical Practice: A Review of Market-Available Tools for Clinical Practice

Giordano, A., Romero-Mascarell, C., González-Suárez, B., & Guarner‐Argente, C. (2025) · Digestive Diseases and Sciences · doi:10.1007/s10620-025-09099-4 · Cited by 13

AI Revolution in Radiology, Radiation Oncology and Nuclear Medicine: Transforming and Innovating the Radiological Sciences

Carriero, S., Cannella, R., Cicchetti, F., Angileri, S. A., Bruno, A., Biondetti, P., Colciago, R. R., D’Antonio, A., Pepa, G. D., Grassi, F., et al. (2025) · Journal of Medical Imaging and Radiation Oncology · doi:10.1111/1754-9485.13880 · Cited by 16

AID-RT: Standardising Artificial Intelligence Documentation in RadioTherapy with a domain-specific model card

Montero, A. M. B., Huet‐Dastarac, M., Herranz-Hernández, S. M., Tengler, B., Buhl, E. S., Galapon, A., Cárdenas, C. E. S., Fusella, M., Herbin, G., de Hond, Y. J. M., et al. (2026) · Physics and Imaging in Radiation Oncology · doi:10.1016/j.phro.2026.100940 · Cited by 4

Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance

Vandewinckele, L., Claessens, M., Dinkla, A. M., Brouwer, C. L., Crijns, W., Verellen, D., & van Elmpt, W. (2020) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2020.09.008 · Cited by 341

Advancing automation in radiation therapy through artificial intelligence : decision support and uncertainty estimation

Huet‐Dastarac, M. (2024) · Digital Access to Libraries

Artificial Intelligence in Radiotherapy: From Technical Automation to Assisted Clinical Decision-Making. A Literature Review

Bouziane, J., Sadiki, E. M., Soussy, K., Khalfi, S., Hassani, W., Farhane, F. Z., Alami, Z., & Bouhafa, T. (2025) · World Journal of Advanced Research and Reviews · doi:10.5281/zenodo.18411458 · Cited by 1

Artificial intelligence across oncologic theranostics: evidence for patient stratification, dosimetry, and adaptive radiopharmaceutical therapy

Banala, M., Ezuddin, S., Foley, M., & Kuker, R. A. (2026) · Frontiers in Nuclear Medicine · doi:10.3389/fnume.2026.1935247 · Cited by 1