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Using Artificial Intelligence for Optimization of the Processes and Resource Utilization in Radiotherapy.

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Artificial intelligence and machine learning can streamline radiotherapy processes in low- and middle-income countries (LMICs). These technologies reduce human effort, improve decision-making, and free up radiation oncologists for patient care and research.

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Area of Science:

  • Oncology
  • Medical Physics
  • Health Informatics

Background:

  • Radiotherapy (RT) planning and delivery are complex, time-intensive processes requiring multidisciplinary expertise.
  • Low- and middle-income countries (LMICs) face unique challenges in providing advanced RT due to resource constraints.
  • Optimizing RT workflows is crucial for improving cancer care accessibility and outcomes in LMICs.

Approach:

  • A comprehensive literature review was conducted using PubMed and reference lists.
  • Search terms included AI, machine learning, deep learning, automation, knowledge-based planning, radiotherapy, and LMICs.
  • Twenty relevant research items focusing on enhancing RT processes were analyzed.

Key Points:

  • AI and machine learning offer significant potential to enhance RT efficiency by automating repetitive tasks and reducing human error.
  • These technologies can aid in decision-making processes within RT planning and delivery.
  • Implementation of AI/ML can allow radiation oncologists to focus on complex patient interactions, education, and research.

Conclusions:

  • AI and machine learning are poised to revolutionize RT workflows, particularly in resource-limited settings like LMICs.
  • Adoption of these technologies can lead to more efficient, precise, and accessible cancer treatment.
  • Further research and implementation are needed to fully realize the benefits of AI/ML in global RT.