How far are we from understanding phygital healthcare convergence? Building an AI knowledge map grounded in bibliometric metadata

Initially prevalent within the marketing domain, the increasing convergence of the physical and digital realms, commonly termed the "phygital" concept (Batat, 2024), is revolutionizing various sectors, including healthcare. This influence is exemplified by prominent initiatives in this field such as the Rome Technopole project “Phygital Twin Technologies for Innovative Surgical Training & Planning” using the phygital concept by developing digital and physical technologies for planning and training surgeons. The phygital paradigm creates hybrid experiences blending real-world interactions with virtual ones, offering new opportunities for learning and practice. This can be done using various technologies like Digital Twins (DTs), Augmented Reality (AR), Virtual Reality (VR), and Artificial Intelligence (AI). DTs and virtual representations of physical entities, processes, or systems, are key elements in this context, providing simulated environments for training, planning, and analysis. However, the phygital concept extends beyond DTs, encompassing a broader ecosystem of technologies. This research delves into this ecosystem using an innovative methodology that combines traditional metadata (author’s keywords) with the power of AI to create a knowledge map (KM) on the phygital concept in healthcare to find convergence relationships of the different concepts. KMs are graphical representations of knowledge, where nodes represent concepts and edges represent relationships between them. KMs can help researchers to identify areas and research landscapes within a specific field or topic. Bibliometric analysis, which has traditionally relied on citation counts and co-occurrence analysis, is commonly used to understand research landscapes. However, this approach has various limitations (see e.g. Haustein & Larivière, 2014). This research addresses this gap by utilizing AI-generated KM while concurrently preserving bibliometric source metadata. To create the KM, we leverage the capabilities of the AI by Google called Gemini 2.0 (G2, https://gemini.google.com/app). Base the KM creation to the bibliometric metadata ensures the reliability of the generated knowledge by grounding it in verifiable data sources and mitigating potential biases that may arise from exclusive reliance on AI- only information. This approach offers a more comprehensive perspective than conventional bibliometric methods by enabling the identification of prominent research areas, subtle relationships and emerging subfields on the phygital in the healthcare context.