Strategic Perspectives on Artificial Intelligence Applications in Ocular Oncology: Managerial Insights and Conceptual Use Cases

Abstract Purpose A pivotal dimension of healthcare’s digital transformation lies in the progressive integration of information and communication technologies (ICT), with artificial intelligence (AI) emerging as a cornerstone of this evolution. The overarching objective of the AI‑driven initiative proposed in this study is to strengthen healthcare organizational governance by strategically integrating advanced technological solutions. Among the major technological advances in medicine, the deployment of AI‑driven solutions within ocular oncology (OO)—often in close connection with radiology—has become a critical and increasingly prioritized area of innovation in ophthalmology. This conceptual study examines the strategic opportunities for deploying AI technologies from a managerial perspective, with the objective of enhancing healthcare processes through the implementation of AI‑based specialized applications. The authors present illustrative examples drawn from their own clinical experience with the use of AI in stereotactic radiosurgery planning for intraocular tumors. Methods Before implementing AI‑driven initiatives, healthcare leaders must assess which use cases offer the highest strategic and economic value, given the substantial resources such projects require. To systematically identify these use cases, this study adopts the Design Science Research (DSR) methodology. DSR provides a structured framework for analyzing organizational needs at the Enterprise Architecture (EA) level, enabling the alignment of functional domains and technological components with prioritized use cases. Through this qualitative, design‑oriented approach, the identification of a set of conceptual use cases can be achieved, ensuring that critical application requirements are elicited and thereby substantiating the feasibility and relevance of AI‑enabled solutions in advancing strategic objectives and contributing to the overarching vision of healthcare digital transformation. Results The research methodology enabled the identification of six conceptual use cases within OO as the primary domain, incorporating linkages to radiology and supported by illustrative cases drawn from the authors’ clinical experience. Through the methodology approach, the functional and structural components of ICT were systematically aligned with the established linkages between healthcare management imperatives and the prioritized use cases. As a conceptual proposition, the envisioned specialized applications integrate AI modules or agents into existing health information systems, with a future capability to support three‑dimensional imaging data for use in augmented and/or virtual reality platforms. Conclusion By strategically applying AI‑driven solutions to automate high‑need, non‑manufacturing processes in healthcare—such as those in OO—and by employing a DSR‑based framework integrated with an EA approach to systematically develop conceptual use cases, following successful implementation and sustained operational performance, healthcare organizations may enhance service‑delivery efficiency, reduce reliance on advanced clinical specialists, and support improved patient‑centered outcomes and satisfaction. However, even with the growing support offered by AI‑based systems during clinical assessment, the ultimate authority and accountability for medical decisions continue to rest solely with physicians—and, in radiology‑related settings, with the responsible radiology professionals.