| Summary: | This research explores the innovative application of Artificial Intelligence (AI), particularly Generative Pre-trained Transformer (GPT) models, in designing culturally sensitive hospitals for rural Kenya. The research addresses the critical need for improved healthcare infrastructure in underserved areas, focusing on the potential of AI to create efficient, adaptable, and contextually appropriate hospital designs. The study employs a mixed-methods approach, combining qualitative analysis of cultural practices and healthcare needs with quantitative data on environmental factors and health statistics. A GPT model is developed and fine-tuned on a comprehensive dataset of Kenyan cultural information, healthcare data, and architectural knowledge. This AI model is then used to generate hospital design concepts that are evaluated against newly developed cultural sensitivity metrics. Key findings demonstrate the potential of AI to significantly reduce design time, improve space utilization, and enhance cultural appropriateness in hospital designs. The thesis also highlights the importance of human-AI collaboration, with local experts and community representatives playing crucial roles in refining and implementing AI-generated concepts. Challenges identified include data quality and availability in rural settings, the need for ongoing model refinement, and the importance of establishing ethical guidelines for AI use in healthcare design. The thesis concludes with a set of recommendations for implementing AI-driven, culturally sensitive hospital design processes in rural Kenya, including the development of specialized AI models, and establishment of collaborative design methodologies. These findings have significant implications for improving healthcare infrastructure in resource-constrained settings and offer a model for culturally sensitive, AI-driven architectural design in developing contexts globally.
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