Artificial intelligence (AI) has attracted a lot of attention in radiation therapy recently. With advancements in deep learning techniques enabled by multi-layered neural networks, AI has demonstrated its successes in solving a number of challenging clinical problems with substantial improvements over conventional methods. Artificial intelligence has been applied in five generic fields of molecular imaging and radiation therapy, including PET instrumentation design, PET image reconstruction quantification and segmentation, image denoising (low-dose imaging), radiation dosimetry and computer-aided diagnosis.

We are seeing a strong rise in the use of AI in various radiotherapy applications, including medical imaging reconstruction, tumor and organ-at-risk segmentations, automated treatment planning, and automatic machine or treatment plan quality assurance. AI has also opened up new opportunities to tackle some problems that have proven too challenging for conventional machine learning techniques. For instance, AI may be used to build models with intelligence to solve problems in a human-like fashion. The use of AI in clinical radiation therapy is expected to generate valuable impacts on treatment accuracy, efficiency, and safety, which we hope will eventually translate into benefits for patient care.

This Special Issue will focus on methodological advancements of artificial intelligence in molecular imaging and radiotherapy. We are looking for contributions that describe AI models in medical imaging from basic research to the clinical setting. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

• AI-based autonomous decision systems and their applications in radiotherapy
• AI in radiomics for radiotherapy of malignant brain tumors
• AI in medical physics and radiotherapy and its future applications
• AI in image-guided radiotherapy for treatment and target localization
• AI in radiotherapy for motion tracking and its applications
• AI-driven planning systems for evaluation of online adaptive radiotherapy and its applications
• AI in ultrasound-guided radiotherapy and its applications
• AI in PET/CT imaging-targeted biopsy techniques for clinical applications
• AI and radiomics in neuro-oncology imaging for clinical applications