Investigating an Age-Inclusive Medical AI Assistant with Large Language Models : User Evaluation with Older Adults

Sammanfattning

The integration of Large Language Models (LLMs) such as GPT-4 and Gemini into healthcare, particularly for elderly care, represents a significant opportunity in the use of artificial intelligence in medical settings. This thesis investigates the capabilities of these models to understand and respond to the healthcare needs of older adults effectively. A framework was developed to evaluate their performance, consisting of specifically designed medical scenarios that simulate real-life interactions, prompting strategies to elicit responses and a comprehensive user evaluation to assess technical performance and contextual understanding. The analysis reveals that while LLMs such as GPT-4 and Gemini exhibit high levels of technical proficiency, their contextual performance shows considerable variability, especially in personalization and handling complex, empathy-driven interactions. In simpler tasks, these models demonstrate appropriate responsiveness, but they struggle with more complex scenarios that require deep medical reasoning and personalized communication. Despite these challenges, the research highlights the potential of LLMs to significantly enhance healthcare delivery for older adults by providing timely and relevant medical information. However, to realize a truly effective implementation, further development is necessary to improve the models’ ability to engage in meaningful dialogue and understand the nuanced needs of an aging population. The findings underscore the necessity of actively involving older adults in the development of AI technologies, ensuring that these models are tailored to their specific needs. This includes focusing on enhancing the contextual and demographic awareness of AI systems. Future efforts should focus on enhancing these models by incorporating user feedback from the older population and applying user-centered design principles to improve accessibility and usability. Such improvements will better support the diverse needs of aging populations in healthcare settings, enhancing care delivery for both patients and doctors while maintaining the essential human touch in medical interactions.