Artificial intelligence-powered innovations in radiotherapy: boosting efficiency and efficacy.

Area of Science:

• Oncology and Medical Physics
• Artificial Intelligence in Healthcare
• Radiotherapy Optimization

Background:

• Cancer incidence is rising globally, increasing the demand for effective treatments.
• Radiotherapy (RT) is a cornerstone of cancer care but faces challenges in efficiency and resource allocation.
• There is a critical need to improve the speed and effectiveness of RT delivery.

Purpose of the Study:

• To demonstrate how Artificial Intelligence (AI) enhances radiotherapy efficiency, particularly in target delineation and treatment planning.
• To introduce the concept of All-in-One RT for improved workflow efficiency.
• To present Radiotherapy Digital Twins (RDTs) as a tool for personalized treatment and efficacy evaluation.

Main Methods:

• Review and perspective on the integration of AI in RT processes.
• Conceptualization of All-in-One RT workflows.
• Exploration of RDTs using patient-specific data and AI for precision medicine.

Main Results:

• AI significantly improves efficiency in time-consuming RT tasks like target delineation and treatment planning.
• All-in-One RT offers a potential pathway to substantially boost overall RT efficiency.
• RDTs facilitate personalized treatment strategies and robust evaluation of therapeutic outcomes.

Conclusions:

• AI and RDTs are poised to transform cancer radiotherapy, enhancing both efficiency and precision.
• These advanced technologies promise to make RT more accessible and effective worldwide.
• The integration of AI and digital twins represents a significant leap forward in oncological care.