Inside the Engine: How to Train Oncology AI Without “Cheating”
Data quality determines everything. Sophisticated algorithms cannot compensate for biased or poorly curated datasets. But once you have the data, how do you train a model that is safe for patients? The “Garbage In, Garbage Out” Reality In radiation oncology, data acquisition is uniquely complex. We aren’t just dealing with spreadsheets; we are dealing with […]