1184 Human vs. Machine: Concordance and Conflict in Spinal Metastasis Management

Abstract Aim To evaluate the concordance between ChatGPT-4 and a spinal multidisciplinary team (MDT) in management decisions for metastatic spinal disease and to explore the potential role of large language models (LLMs) as decision-support tools in oncologic spine care. Frameworks such as the Spine Instability Neoplastic Score (SINS) and the NOMS model guide decisions, but variability persist. Method A retrospective analysis was performed on 100 anonymized adult cases referred to a spinal MDT for spinal metastases with suspected instability or metastatic spinal cord compression (MSCC). Ten additional cases were used to calibrate model input. Each case summary, including demographics, tumor histology, SINS, Karnofsky Performance Scale, and Tokuhashi estimates, was presented identically to ChatGPT-4 (OpenAI, March 2025 snapshot) and the MDT. Management recommendations: surgical vs. palliative (non-curative therapy including radiotherapy, systemic treatment, or best supportive care), were compared using Cohen’s κ, and discordant cases underwent qualitative analysis. Results ChatGPT and the MDT were concordant in 86 % of cases (κ = 0.66, p < 0.001), indicating substantial agreement. Concordant recommendations included surgery in 26 % and palliative care in 60 %. Discordance occurred in 14 % of cases, typically involving younger patients or rare tumor histologies. ChatGPT emphasized mechanical instability and high SINS scores, whereas the MDT weighted systemic prognosis more heavily. Conclusions ChatGPT-4 achieved substantial concordance with expert MDT decision-making in metastatic spinal disease. While it aligned well in structurally clear-cut cases, discordance in prognostically complex scenarios underscores the need for prognosis-aware, dynamically validated AI models. These findings support AI as an adjunct—not a replacement—for multidisciplinary clinical workflows.