Artificial intelligence (AI) is transforming radiotherapy by improving accuracy, efficiency, and personalisation in treatment planning and delivery. From automated contouring to adaptive therapy, AI is helping clinicians make faster, more precise decisions while reducing variability and workload. Below are key ways AI is shaping radiotherapy, along with the challenges of its adoption:
Key Ways AI is Transforming Radiotherapy
1. AI-Assisted Contouring
- Before: Clinicians manually outline tumours and organs-at-risk (OARs) on imaging scans, a time-consuming process open to inter-observer variability (differences in how different specialists contour the same scan).
- With AI: Deep learning models can automatically segment tumours and organs at risk (OARs) on CT, MRI, and PET scans within minutes, providing consistent and accurate contours while still allowing expert oversight.
- Impact: AI-assisted contouring saves time, reduces errors, and standardises treatment planning.
2. Optimised Treatment Planning
- Before: Creating a radiation treatment plan required iterative adjustments by multiple specialists (Radiation Oncologist, Radiologist, Medical Physicist, Dosimetrist) to balance tumour coverage and organ protection.
- With AI: Machine learning algorithms predict optimal radiation dose distributions based on historical patient data and clinical guidelines, significantly speeding up treatment planning.
- Impact: AI enables personalised, high-quality plans in less time, improving workflow efficiency.
3. Adaptive Radiotherapy (ART)
- Before: Traditional radiotherapy plans are static, meaning they don’t account for changes in tumour size, shape, or patient anatomy during treatment.
- With AI: AI-driven adaptive radiotherapy systems analyse daily imaging data, detecting anatomical changes and automatically adjusting radiation plans accordingly.
- Impact: More precise treatments that adapt to patient changes, reducing radiation exposure to healthy tissue and improving outcomes.
4. Predictive Analytics for Patient Outcomes
- Before: Clinicians relied on general guidelines and past cases to predict treatment responses.
- With AI: AI models analyse large datasets to predict which patients will respond best to specific radiotherapy protocols, helping tailor treatments for better efficacy.
- Impact: AI enables precision oncology, improving patient survival and quality of life.
5. AI-Guided Motion Management
- Before: Motion during treatment (e.g., breathing, organ shifts) led to radiation targeting challenges.
- With AI: Real-time AI tracking systems monitor patient movements and anticipate motion patterns, such as respiratory cycles, to adjust treatment delivery accordingly.
- Impact: Reduces radiation exposure to healthy tissues and improves tumour targeting.
6. Automating Quality Assurance & Workflow
- Before: Manual quality checks were required to ensure treatment plans met safety standards.
- With AI: AI-powered quality assurance tools detect errors, inconsistencies, and deviations in treatment plans, alerting clinicians before delivery.
- Impact: Enhances patient safety and treatment reliability.
7. Predictive Maintenance for Radiotherapy Machines
- Before: Routine maintenance followed fixed schedules or reactive repairs, leading to unexpected machine downtime and treatment delays.
- With AI: AI-driven predictive maintenance uses data analytics, AI, and machine learning to anticipate potential failures before they occur. Analysing real-time machine performance data to detect potential issues before they cause failures, allowing for proactive servicing.
- Impact: Reduces unplanned downtime, improves treatment continuity, extends equipment lifespan, and enhances patient safety by ensuring machines operate at peak performance.
Key Challenges & Considerations in AI Adoption
- Regulatory and Ethical Concerns: AI must comply with safety regulations (e.g. CDRH, MHRA) and address ethical issues such as patient consent (including data privacy, usage, and transparency in how AI is being used in their diagnosis or treatment).
- Bias and Generalisation: AI models trained on specific populations may struggle to perform accurately across diverse demographics (e.g. age, gender, ethnicity). This algorithm bias arises when AI unintentionally favours or discriminates against certain groups due to imbalanced training data or design flaws. As a result, underrepresented populations may receive less accurate or less reliable outcomes.
- Interpretability (how easily humans can understand and explain how an AI model makes decisions): AI models can be ‘black boxes’, so transparent, explainable systems are needed for clinicians to trust them.
- Clinical Integration: Integrating AI into existing clinical workflows and practices can be difficult. AI tools must work seamlessly with current radiotherapy planning systems, imaging technologies, and electronic health records (EHR). requiring proper training and cooperation among healthcare professionals.
- Data Privacy and Security: AI tools must comply with privacy regulations and ensure secure handling of sensitive patient data.
- Validation and Reliability: AI systems need to be rigorously tested and validated to demonstrate accuracy and safety before being implemented in real-world settings, requiring ongoing clinical trials, studies and continuous monitoring and updates to new data.
The Future of AI in Radiotherapy
AI is expected to play a growing role in real-time treatment adaptation, AI-powered radiation beam shaping, and fully automated radiotherapy workflows. As research and technology advance, AI-driven radiotherapy will continue improving precision, efficiency, and patient outcomes.