The Center for Data Innovation recently spoke with John Sanwo, CEO of Wearable Dose, a Maryland-based company developing a wearable radiation-monitoring device and AI-powered platform for cancer treatment and safety applications. Sanwo explained how its wireless sensor patch measures radiation exposure and uses AI-driven analysis to help clinicians verify that patients receive the intended dose during treatment.
David Kertai: What does Wearable Dose offer?
John Sanwo: Radiation therapy is one of the most common cancer treatments, but clinicians currently have no simple way to measure the actual radiation dose a patient receives on the skin or at the point where the beam enters the body. Instead, they rely on treatment-planning software and machine-positioning systems that estimate the dose before treatment begins. However, patient movement, positioning changes, and small delivery variations can cause differences between the planned and actual dose. This gap is especially important in pediatric care and high-dose treatments, where even small differences can have significant effects.
Wearable Dose addresses this gap with a small, skin-worn wireless patch that measures radiation exposure in real time during treatment. The patch uses a flexible sensor that continuously collects dose data and sends it to our AI-powered platform, which analyzes the information and displays it through a clinician dashboard. Together, the sensor and software let clinicians see the dose a patient is actually receiving, not just the planned estimate. The system supports adult and pediatric radiation therapy and also applies to radiation safety monitoring in chemical, radiological, and nuclear environments where real-time exposure tracking matters.
Kertai: How does your device measure radiation?
Sanwo: The patch-based device measures radiation directly as it reaches the patient. When radiation hits the sensor, it generates an electrical signal in real time, allowing immediate measurement without intermediate conversion steps. This enables fast and accurate detection during treatment.
The device includes a sensor array that shows both the total dose and how radiation distributes across the treatment area. Because the sensor detects key radiation particles, such as photons, electrons, and protons, it works across major radiation therapies, including Volumetric Modulated Arc Therapy and Stereotactic Body Radiation Therapy.
Kertai: How does your AI platform analyze sensor data and present it to users?
Sanwo: Our AI platform combines data from the patch with medical imaging and radiation machine settings to estimate dose delivery in real time. It continuously compares those estimates with the measurements collected by the patch and updates results throughout treatment.
Clinicians access this information through a live dashboard that compares planned versus delivered dose at the skin and beam-entry region. If the delivered dose moves outside safe limits, the system sends an alert. This shifts radiation therapy from post-treatment verification to real-time quality assurance during treatment.
Kertai: What industries use your device, and could you provide some examples of use cases?
Sanwo: Our main use is in radiation oncology, where clinicians need immediate confirmation that treatment is delivered as intended. It is especially important in pediatric care, where patients are more sensitive to radiation and treatment margins are extremely tight.
The system also supports adaptive radiotherapy, where clinicians adjust treatment plans during a course of therapy based on real-time feedback. In proton therapy, where radiation deposits energy at a precise depth inside the body, the device helps verify that the beam enters the body as expected.
Beyond medicine, the device has potential to help radiation monitoring for military personnel, first responders, nuclear facility workers, and emergency response teams operating in radiological environments.
Kertai: What challenges have you faced in developing a wearable radiation monitoring device?
Sanwo: The biggest challenge has been combining materials science, electronics, AI, and clinical requirements into a single reliable system. We had to ensure the sensor works accurately across different radiation types and dose levels, and that it performs reliably in radiation-treatment rooms with high electronic noise.
We also faced regulatory and workflow challenges. We are currently working through the U.S. Food and Drug Administration’s clearance process and Breakthrough Device Designation pathway. At the same time, we are focused on making the device easy to integrate into clinical workflows so it does not slow down treatment or add complexity for clinicians. Ultimately, we aim to make real-time radiation verification a standard part of cancer treatment so clinicians can deliver safer and more precise care.
