A study reported a transparent AI model that reached 95% accuracy in selecting between approved PD-1 therapies nivolumab and pembrolizumab for patients with recurrent or metastatic head and neck squamous cell carcinoma. The work framed the clinical decision as a difficult judgment call and evaluated AI performance for therapy choice. The “transparent” framing indicates the model’s decision process was designed to be interpretable rather than purely predictive, aligning with growing regulatory and clinician demand for explainability in AI-assisted care. The dataset and evaluation design determine how transferable the result may be, but the headline metric places the approach near the top tier of reported AI diagnostic performance. If validated prospectively, such tools could reduce variability in treatment selection and accelerate personalized decision support in oncology where biomarkers are not always sufficient for nuanced therapy ranking.