# Generative Artificial Intelligence and Risk Appetite in Medical Decisions in Rheumatoid Arthritis
## 4 Discussion  
This study explored the risk appetite of five Generative Artificial Intelligence (GenAI) systems within the context of medical decisions, utilizing two distinct methodologies: an adapted questionnaire (GRiPS) and the Time Trade-Off (TTO) method. The results offer a nuanced perspective on how these technologies approach uncertainty and risk. A key initial finding is the convergence of GenAIs in the study's first phase: all systems independently identified the same Rheumatid Arthritis cases as having the most favorable (Case A) and unfavorable (Case B) prognosis among the 20 presented. This suggests a shared fundamental understanding of prognostic factors in this pathology, likely derived from the extensive medical datasets on which they were trained. However, beyond this basic prognostic assessment, significant divergences emerged regarding risk appetite. Analysis using the adapted GRiPS questionnaire showed that while GenAIs do not differ significantly in their risk approach when presented with a specific clinical context (Good or Poor Prognosis), they exhibit significant differences in general risk propensity (Scenario P). In this general scenario, ChatGPT proved significantly less risk-averse than all other tested GenAIs, whereas Qwen and DeepSeek showed the opposite tendency. This raises the question of whether the specificity of the clinical context anchors the GenAIs' responses, reducing the variability observed in a more abstract scenario. Comparing the results obtained via the two methodologies (adapted GRiPS and TTO) reveals that the risk assessment method influences the apparent risk profile of the GenAIs. Whereas in the GRiPS test (general scenario), ChatGPT emerged as the least risk-averse, in the TTO test, Perplexity demonstrated significantly higher risk tolerance compared to Gemini, being more willing to accept a smaller number of years in perfect health in exchange for eliminating the risk of moderate pain. Perplexity also exhibited the greatest variability in TTO responses. This discrepancy between methodologies suggests that GenAIs may process and respond differently to different risk-framing methods--either as a general propensity (GRiPS) or as a direct trade-off between quantifiable outcomes (TTO). Despite differences, the TTO method revealed a strong general tendency towards risk aversion among all tested GenAIs. The mean and median utility scores very close to 1.00 indicate that, when faced with a trade-off between lifespan and quality of life (avoiding moderate pain), the GenAIs prefer, in most cases, not to sacrifice years of life unless the alternative guarantees perfect health for the maximum duration. This could reflect implicit programming or learned behavior to prioritize life preservation or avoid recommendations  
Figure 2: Distribution of TTO-derived risk tolerance across GenAI systems for patients aged Y years. The boxplot represents the willingness to trade lifespan in order to avoid moderate pain (VAS \(\geq\) 4) in RA patients. The mean (\(\times\)) and individual outliers are shown for each GenAI system. A TTO value of 0 corresponds to continued current therapy. involving quantifiable losses, possibly as a safety measure. The Friedman test suggested potential differentiation in the TTO approach based on patient age, particularly for the 55-year-old group (\(p\) = 0.050), where Perplexity showed the highest risk tolerance (mean rank 4.31) and Gemini the lowest (mean rank 2.25). Although not strongly statistically significant, this aligns with the significant difference observed globally between Perplexity and Gemini in paired tests. The study also investigated the reliability of GenAI responses. Coefficients of variation generally indicated good intra-rater consistency, with the notable exception of Gemini in the good prognosis scenario (A). This specific inconsistency may warrant further investigation--is it possible that Gemini has difficulty calibrating its responses in situations perceived as already having a favorable outcome?