By Shiv Patel
On the surface, radiation oncology seems a science-based discipline — dose calculations, precision imaging, molecular biomarkers. But the more I continue with my research at Mayo Clinic this summer, the more I am reminded that behind every equation and algorithm is a network of people whose voices educate, complement, or are absent from the care process.
My project focuses on making personalized radiation oncology more accessible, especially to medically underserved populations. That starts with mapping the people — the stakeholders — directly and indirectly involved. On the front lines are the radiation oncologists, clinical data scientists, physicists, and technicians who are making daily treatment plan decisions. Yet beyond them are the patients, their families, insurance providers, hospital administrators, policymakers, and even the technology companies that develop the AI models we utilize to stratify treatment.
A few of these stakeholders, including oncologists and researchers, possess a loud voice within the academic literature. They are the individuals driving innovation, and I have read dozens of papers by specialists creating models that foresee radiosensitivity or include genomics in treatment planning. But I’ve noticed that these voices tend to come from resource-rich institutions — Mayo, MSK, Stanford. And what’s often left out are the perspectives of community clinics, rural hospitals, and minority-serving institutions — the kinds of places where patients may not have access to genomic classifiers, or where infrastructure doesn’t support the latest AI software. Even more rarely do we hear from the patients themselves, especially those of marginalized populations, whose stories may not be told in peer-reviewed literature but who carry the lived experience of cancer care disparity.
That’s where my personal connection comes in. I was raised in rural Florida, and I’ve seen how zip codes can determine health outcomes. I’ve watched family members and neighbors struggle to navigate systems that were not built for them — where referrals took weeks, specialists were hours away, and preventive care wasn’t even a concept. When I read articles about genomic-guided radiation planning, I’m excited about the science but troubled by the divide. Who gets these cutting-edge treatments, and who gets left behind?
My literature review returned clinical and technical as the dominant perspectives — no surprise, given the discipline of radiation oncology. But the public health perspective is embryonic. Freeman and Huo (2024) make this observation in their explanation of how decreasing disparities in precision medicine is not a function of inserting race as a variable — it is a function of restructuring and reorienting care. That’s the perspective I hope to contribute: not as a possessor of all knowledge, but as a bridge between the technical and social, someone who can create room for both models and people, results and accessibility.
Where do I fit? Of the four roles for Global Scholars, I e nvision myself rotating through all of them, but I am most firmly a connector. I want to bridge disciplines (oncology and public health), communities (academic institutions and underserved patients), and narratives (lived experience and clinical outcomes). I want to have a seat at the table where genomic sequencing and equity are mentioned in the same sentence, not as an afterthought, but as integral components of the future of care.
I hope that this summer experience will not just make me understand AI-driven radiation therapy. I would like to gain a more profound understanding of the ecosystems around it — the voices amplified and the ones needing to be heard louder.
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