# Generative Artificial Intelligence and Risk Appetite in Medical Decisions in Rheumatoid Arthritis
## 4 Discussion
### Implications and Limitations  
The results highlight the importance of understanding that different GenAI systems may have distinct risk profiles, and these can vary depending on the clinical context and how risk-related questions are framed. This has significant implications for integrating GenAI into the medical decision-making process, either directly by clinicians or indirectly by patients using them for information. A uniform risk approach cannot be assumed across different AI platforms. This study, while offering valuable contributions to understanding how Generative Artificial Intelligence (GenAI) approaches risk in a medical context, has inherent limitations that require careful consideration when interpreting and generalizing its results. These limitations are important for defining the scope of the conclusions and guiding future research. A significant limitation is the restricted sample of evaluated GenAI systems. The study focused on five specific platforms (ChatGPT 4.5, Gemini 2.0, Qwen 2.5 MAX, DeepSeek-V3, and Perplexity), which, while popular and advanced at the time of research, do not represent the entire GenAI landscape. The field of Artificial Intelligence is highly dynamic, with new models and versions constantly emerging, each potentially having different architectures, training datasets, and design philosophies, including regarding safety and uncertainty management. Therefore, the risk profiles identified in these five GenAIs cannot be automatically extrapolated to other existing or future systems. Secondly, the exclusive focus on a single pathology, Rheumatid Arthritis (RA), limits the generalizability of the findings. RA, although a suitable model for complex decision-making in chronic disease management, has specific characteristics related to prognosis, therapeutic options, treatment-associated risks, and impact on quality of life. Decision-making processes and risk tolerance can vary substantially in other medical fields, such as oncology (where trade-offs between toxicity and survival are common), acute infectious diseases (where timeliness of decisions is critical), or palliative care. The risk appetite manifested by GenAIs in RA scenarios might not be replicated similarly in these different contexts. A third important limitation is the time-bound nature of the results. Data were collected within a specific timeframe, between 1 and 15 April 2025. GenAI models undergo continuous training, updating, and fine-tuning processes, including techniques like RLHF (Reinforcement Learning from Human Feedback). These updates can alter not only the AI's knowledge base but also its decision-making behavior, including how it weighs risks and benefits. Thus, the risk profiles observed in the study represent a snapshot in time and may change as the models evolve. A fourth limitation is related to different ways the prompts are created. Future work should investigate how variations in prompt phrasing may alter AI risk-taking profiles, potentially affecting the generalizability of our findings. Finally, the methodology based on text queries necessarily simplifies the complexity of real-world clinical interactions. Medical decisions are not based solely on written information exchange but involve dialog, clarifications, interpretation of non-verbal cues (in physician-patient interaction), integration of data from multiple sources (history, clinical examination, and investigations), and nuanced clinical judgment. Simulating this complex process via text prompts, while necessary for study standardization, cannot fully capture how a GenAI might interact or manifest its \"risk appetite\" within an integrated clinical workflow or a dynamic conversation with a clinician or patient. Responses to predefined prompts may not fully reflect the AI's ability to navigate uncertainty in less structured situations. Acknowledging these limitations is important.