This prospective study will test artificial intelligence (AI) and machine learning (ML) decision support tools. This tool is designed to help doctors, physicists and other staff during pre-treatment peer review, a step where treatment plans are checked before a patient begins care.

The system highlights summaries showing how different providers may vary in their treatment planning (provider-variability summaries) and points out the best signals or warning signs to look for (optimal cues). By drawing attention to these patterns and cues, the tool aims to help reviewers spot possible treatment-planning mistakes earlier, reduce the chance of errors, and improve overall patient safety.

As radiation therapy (RT) becomes more complex, the number of possible error pathways increases. AI-supported peer review can help catch errors that might otherwise go unnoticed and promote consistent, equitable safety standards across both rural and urban clinics.

Radiation therapy (RT) is used in about 50% of cancer patients and usually given in outpatient clinics. Newer technologies such as intensity-modulated radiation therapy (IMRT), Volumetric Modulated Arc Therapy (VMAT), and Image-guided radiation therapy (IGRT), improve treatment by better protecting normal tissue and higher dose in target areas. However, they are more complex and require very precise definition of tumor targets and normal tissues. Even small errors in outlining these areas can lead to under-treating the tumor or over-treating healthy tissue. Studies show that errors in defining target areas have increased in modern radiation oncology. Because these treatments are more cognitively demanding, the risk of planning errors has increased and, in some cases, errors can cause serious harm.

Pre-treatment peer review is where a multidisciplinary team reviews the treatment plan before therapy begins is an important safety step and is strongly recommended. It is most effective when done before treatment starts, since making corrections later can cause treatment delays, rushed changes, and added The potential impact on patient safety is substantial.

Because of the growing complexity and workload, there is a need to strengthen and partially automate pre-treatment peer review. AI/ML decision-support tools can help by summarizing key information, highlighting unusual plan features, and drawing attention to areas of potential risk. These tools do not make treatment decisions. Instead, they provide analytics and visual summaries to support clinicians and reduce cognitive burden.

Because the tool also highlights differences in how providers plan treatments, it may help identify variation in care and bring attention to potential health disparities, supporting future efforts to improve equity in radiation oncology.

In order to participate in this study a subject must meet all of the eligibility criteria outlined below.

Inclusion Criteria:

Providers only

• ≥18 years
• Peer-review attendees at participating clinics

Patients only

• ≥18 years
• All patients with prostate cancer radiation therapy cases treated at participating sites (no intervention delivered to patients)

Exclusion Criteria:

Providers only

• Providers unwilling/unable to comply with study procedures; sites unable to implement the workflow or provide required outcomes.

Patients and Providers

• Has dementia, altered mental status, or any psychiatric or co-morbid condition prohibiting the understanding or rendering of informed consent