Why This Matters in Daily Practice

Artificial intelligence is rapidly finding its way into radiation oncology workflows — from image reconstruction to treatment planning. For clinicians, the promise is appealing: faster planning, more consistent dose distributions, and reduced workload.

But as AI tools move closer to routine clinical use, an important question emerges: Are these systems benefiting all patients equally?

Efficiency alone is not enough. In radiation medicine, fairness and safety are just as critical as speed.

What AI Does Well in Radiation Workflows

Across radiation physics workflows, AI excels at handling complex, high-dimensional data that traditional methods struggle with. In simulated radiotherapy environments, AI-assisted planning can dramatically reduce planning time while improving technical metrics such as dose homogeneity and conformity.

From a clinical perspective, this translates into:

·      Faster plan generation

·      Reduced inter-planner variability

·      More time for clinicians to focus on patient-centered decision-making

These advantages explain why AI adoption is accelerating across institutions worldwide.

The Hidden Risk: Algorithmic Bias

While performance gains are impressive, AI systems learn from data — and data are rarely neutral.

If training datasets underrepresent certain demographic groups, AI models may unintentionally produce systematic differences in treatment quality. In radiation therapy, even small disparities in dose distribution can have meaningful clinical consequences.

Simulation studies allow us to explore this risk in a controlled way. When demographic diversity is explicitly modeled, differences in AI-generated treatment quality across population groups can emerge — not due to biology, but due to data imbalance.

This is where fairness-aware AI design becomes essential.

A Key Clinical Pearl

AI should be evaluated not only on average performance, but on how consistently it performs across different patient groups.

In simulated radiation workflows, introducing fairness constraints into AI models can substantially reduce disparities between demographic groups — while preserving efficiency gains. This suggests that bias is not inevitable, but it must be actively addressed during model development and validation.

What Clinicians Should Ask Before Trusting AI

Before integrating AI tools into routine radiation practice, clinicians should feel empowered to ask:

1.    Who was represented in the training data?

2.    Was performance evaluated across diverse patient groups?

3.    Are fairness or bias-monitoring strategies in place?

4.    Is there ongoing validation after deployment?

AI should support clinical judgment — not quietly shape it in unintended ways.

Clinical Takeaway

AI has the potential to transform radiation workflows by improving efficiency and consistency. However, responsible deployment requires deliberate attention to fairness, transparency, and validation.

When designed and evaluated thoughtfully, AI can enhance not only how fast we plan treatments — but how safely and equitably we deliver them to every patient.

Rihem Daas is a radiation and medical physicist working at the intersection of radiotherapy, medical imaging, and artificial intelligence. Her interests focus on responsible AI integration in healthcare, algorithmic fairness, and improving clinical workflows while prioritizing patient safety and equity.