A Comparative Study of Semi-supervised Deep Anomaly Detection and LLMs for Monitoring Patients with Severe Health Status Undergoing Radiotherapy

This study assesses semi-supervised deep anomaly detection (AD) methods and novel zero-shot LLM prompts for identifying prostate cancer patients at risk of severe radiotherapy-induced symptoms via patient-reported outcomes (PROs). While LLMs underperformed compared to semi-supervised AD models in key metrics (e.g., precision, recall, and F1-score), they provided valuable decision explanations and required no training data. This highlights their potential for straightforward clinical deployment without the need for extensive model development.