Unlocking the Potential of Health Data with Decentralised Search in Personal Health Datastores

In the digital age, where health data and digital lives converge, data privacy and control are crucial. The advent of AI and Large Language Models (LLMs) brings advanced data analysis and healthcare predictions, but also privacy concerns. The ESPRESSO project asserts that for AI to be trustworthy and effective in healthcare, it must prioritize user control over corporate interests. The shift towards decentralized personal online datastores (pods) and Solid principles represents a new era of private, controllable Web interactions, balancing AI data protection and machine intelligence. This balance is particularly important for applications involving health data. However, decentralization poses challenges, particularly in secure, efficient data search and data retrieval, that need to be addressed first. We argue that a decentralized search system that provides a large-scale search across Solid pods, while considering data owners' control of their data and users' different access rights, is crucial for this new paradigm. In this paper, we describe how our current decentralized search system's prototype (ESPRESSO) helps to query structured and unstructured personal health data in Solid servers. The paper also describes a search scenario that shows how ESPRESSO can search health data combined with fitness personal data stored in different personal datastores