Large Language Models (LLMs) such as ChatGPT demonstrate significant potential in the medical domain and are often evaluated using multiple-choice questions (MCQs) modeled on exams like the USMLE. However, such benchmarks may overestimate true clinical understanding by rewarding pattern recognition and test-taking heuristics. To investigate this, we created a fictional medical benchmark centered on an imaginary organ, the Glianorex, allowing us to separate memorized knowledge from reasoning ability. We generated textbooks and MCQs in English and French using leading LLMs, then evaluated proprietary, open-source, and domain-specific models in a zero-shot setting. Despite the fictional content, models achieved an average score of 64%, while physicians scored only 27%. Fine-tuned medical models outperformed base models in English but not in French. Ablation and interpretability analyses revealed that models frequently relied on shallow cues, test-taking strategies, and hallucinated reasoning to identify the correct choice. These results suggest that standard MCQ-based evaluations may not effectively measure clinical reasoning and highlight the need for more robust, clinically meaningful assessment methods for LLMs.

The SERENOA project is aimed at developing a novel, open platform for enabling the creation of context-sensitive Service Front-Ends (SFEs). A context-sensitive SFE provides a user interface (UI) that allows users to interact with remote services, and which exhibits some capability to be aware of the context and to react to changes of this context in a continuous way. As a result, such UI will be adapted to e.g. a person's devices, tasks, preferences, abilities, and social relationships, as well as the conditions of the surrounding physical environment, thus improving people's satisfaction and performance compared to traditional SFEs based on manually designed UIs. The final aim is to support humans in a more effective, personalized and consistent way, thus improving the quality of life for citizens. In this scenario, we envisage SERENOA as the reference implementation of a SFE adaptation platform for the 'Future Internet'.