# Generative Artificial Intelligence and Risk Appetite in Medical Decisions in Rheumatoid Arthritis
## 2 Materials and Methods  
In this study, we evaluated the concepts of risk appetite and risk aversion in the context of medical decisions, using five contemporary Generative Artificial Intelligence (GenAI) systems as sources of decisional perspective. We selected the following systems for analysis:  
1. ChatGPT 4.5--Developed by OpenAI, this model represents an advanced iteration of the GPT (Generative Pre-trained Transformer) architecture. It is trained on a large corpus of text and multimodal data, being one of the most widely used GenAI systems globally. The model uses supervised learning and reinforcement learning from human feedback (RLHF) techniques [11]. 2. Gemini 2.0--Developed by Google, this next-generation multimodal model is designed to process and generate diverse content types (text, image, and audio). Gemini utilizes advanced transformer architecture and is trained on diverse data, including specialized medical research [12]. 3. Qwen 2.5 MAX--Developed by Alibaba Cloud, this model represents an advanced version of the Qwen architecture. It is a multilingual model with advanced natural language processing and complex task-solving capabilities, including in the medical domain [13]. 4. DeepSeek-V3--Developed by DeepSeek AI, this model uses an advanced transformer-type architecture and is trained on a large data corpus to provide detailed and contextualized responses. It is noted for its ability to address complex problems in specialized domains [14]. 5. Perplexity--A system based on a large language model, this model is optimized for information retrieval and factual responses. It is differentiated by its ability to access and synthesize current information from multiple sources [15]. All queries were performed between 1 and 15 April 2025, over 4 separate days with at least two days between them, using the same computer connected to the internet via the same IP address in the European Union. All GenAI systems were accessed via their publicly available interfaces without any form of fine-tuning, local deployment, or integration with custom knowledge bases; responses were generated based solely on standardized prompts in isolated sessions to ensure consistency and eliminate prior context influence. The research was structured into three distinct phases, each employing complementary methodologies to evaluate risk tolerance in the medical context. In the initial phase of the study, we developed 20 clinical cases of Rheumatoid Arthritis (RA) with controlled variation in relevant clinical parameters. These parameters included disease duration, patient age, patient sex, number of conventional disease-modifying antirheumatic drugs (DMARDs) previously tried and failed, and number of biological DMARDs previously tried and failed. Each GenAI system was asked to evaluate the 20 cases and identify the case with the most favorable prognosis (hereafter referred to as \"Case A\") and the case with the most unfavorable prognosis (hereafter referred to as \"Case B\"). It is noteworthy that all five GenAI systems independently identified the same cases as having the best and worst prognosis, respectively, providing a consistent basis for subsequent research stages. In the second phase of the study, we assessed risk appetite using an adapted version of the GRiPS (General Risk Propensity Scale), a validated instrument for evaluating general risk propensity [16; 17]. The standard statements from the GRiPS questionnaire were specifically adapted to represent therapeutic statements applicable to a generic patient with Rheumatoid Arthritis (see Table 1). To ensure data consistency and reliability, each GenAI system was queried twelve times in total, in random order: four queries without specifying a medical or non-medical decision context, four queries explicitly related to the decision to initiate a new treatment in the patient with the best prognosis (Case A), and four queries explicitly related to the decision to initiate a new treatment in the patient with the worst prognosis (Case B).