Impact of AI Algorithms on Optimizing Radiotherapy for Cancer Patients

Abstract

Radiotherapy works well for cancer treatment, but it can't reach its full potential due to common mistakes by healthcare teams, less-than-perfect planning tools, and the challenge of giving patients just the right dose. Research using artificial intelligence has developed new methods to improve radiotherapy by helping doctors create better treatment plans, make treatments more accurate, and lower chances of side effects. AI's ML and DL methods are making big changes across radiotherapy processes, from dividing images to calculating dose levels and forecasting how patients will react to treatments. These new technologies use patient data - including their genetic makeup, medical pictures, and medical records - to design custom-made treatment plans that better target where and how the treatment is delivered. The AI system tracks patients' treatment progress by constantly analysing information, which lets doctors change radiotherapy plans right away when the cancer changes in-between treatments. The issues with combining AI into medical care include flawed information, hard-to-read data, and not showing how AI systems make decisions. This study looks at how AI helps improve radiotherapy planning, outlines what works today and what problems need fixing, and suggests ways AI can keep getting better for cancer treatment. Delivering better cancer treatment with precise personalized radiotherapy is how AI can make a big difference to patients' recovery and make cancer treatment run more smoothly.