AI is Smart. Is it Wise? Quantifying the Effect of Patient-Choice (β) on Physical Outcomes

Large language models (LLMs) increasingly guide clinical decisions through population-level evidence, yet they cannot encode individual patient preferences. When treatments yield comparable outcomes, patient choice may drive decisions, though its effect remains unquantified. The Spine Patient Outcomes Research Trial (SPORT)--marked by similar surgical and nonoperative results and substantial crossover--provided a natural experiment to use causal-inference methods to estimate unbiased treatment effects and quantify the contribution of patient choice to outcomes. Using only published aggregate results from SPORT, we conducted two-stage least squares instrumental-variable analysis using randomized treatment assignment as the instrument, with Complier Average Causal Effects (CACE) and E-values assessing sensitivity to unmeasured confounding. Primary outcomes were SF-36 Bodily Pain, SF-36 Physical Function scores, and the Oswestry Disability Index. We decomposed treatment effects into , the biological treatment mechanism, and {beta}, the patient-choice contribution. Aggregate estimates revealed G = 15.7 (0.5) and {beta}G = 7.4 (3.4), with the net difference between surgical and nonoperative treatment effects {Delta} {approx} 0.65. This analysis quantifies a measurable and significant effect of patient choice ({beta}) on physical outcomes. When treatment effects are comparable ({Delta} small), {beta}--a dimension inaccessible to current LLMs trained on -biased population-level evidence--emerges as the dominant driver of decision-making. These findings provide an empirical grounding for informed choice, clarify the limits of LLMs trained on -biased evidence, and quantify a structural constraint in AI-driven clinical decision support. Key messagesO_LIThe effect of patient choice ({beta}) on physical outcomes is real, measurable, and clinically meaningful. C_LIO_LI{beta} becomes the dominant driver of outcomes when biological treatment differences ({Delta}) are small. C_LIO_LILLMs cannot encode {beta} because they are trained on -biased population-level evidence. C_LIO_LIThese findings provide the empirical foundation for informed choice--not just informed consent. C_LI