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

Strategic Perspectives on Artificial Intelligence Applications in Ocular Oncology: Managerial Insights and General Use Cases | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Strategic Perspectives on Artificial Intelligence Applications in Ocular Oncology: Managerial Insights and General Use Cases Robert Furda, Michal Gregus, Pavol Vesely, Jela Valaskova, Peter Kadlic, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8354984/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version 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. Among the most transformative advancements is the deployment of AI-driven methodologies within oncology. Within this context, ocular oncology represents a critical and increasingly prioritized area for innovation. This study concentrates on the possible strategic application of AI technologies from the management perspective with the vision to optimize healthcare processes encompassing, for example, diagnosis, education, therapeutic interventions, and longitudinal monitoring of ocular malignancies. Methods: Prior to initiating AI-driven interventions, healthcare management must rigorously assess which use cases yield the greatest potential for strategic and economic impact, given the substantial resource requirements typically associated with such initiatives. 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, critical application requirements are elicited, 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 process facilitated the identification of several generic use cases within ophthalmology, ultimately converging on ocular oncology as the focal domain. Within this framework, the functional and structural components of ICT were systematically aligned with the established linkages between healthcare management imperatives and the prioritized use cases. The overarching aim of the proposed AI-driven initiative is to advance healthcare management practices through targeted technological integration. For this goal, special applications are envisioned to incorporate the AI-powered modules or AI agents within existing health information systems, in vision supporting also the three-dimensional image data for the augmented and/or virtual reality platforms. Conclusion: AI-driven methodologies should be strategically employed to automate non-manufacturing processes within healthcare domains that exhibit the greatest need for innovation, such as ocular oncology. In this context, informed managerial decision-making can substantially accelerate service delivery by reducing turnaround times, mitigating reliance on highly specialized personnel, and ultimately improving patient-centered outcomes and satisfaction in healthcare services. Ocular Oncology Innovation Management Artificial Intelligence Digital transformation Figures Figure 1 Figure 2 Introduction Artificial Intelligence (AI) has evolved from a conceptual framework introduced in 1956 into a transformative technological paradigm that permeates contemporary discourse. Despite its ubiquity, the inherent complexity of AI remains opaque to many stakeholders. Analogous to the multidimensional nature of human cognition described in psychology, AI encompasses a diverse array of computational and algorithmic components. This overview delineates the historical trajectory of AI without presupposing expertise in computer science or philosophy, emphasizing its current role as a catalyst for innovation in medicine. Far beyond its origins in academic research, AI now actively supports healthcare professionals by enhancing diagnostic accuracy, improving clinical outcomes, and optimizing systemic efficiency on a global scale. Crucially, healthcare management assumes a pivotal function in steering these advancements through evidence-based decision-making and strategic governance.[ 1 – 4 ] Healthcare management assumes a critical role in ensuring that AI technologies contribute not only to clinical decision-making but also to the optimization of operational workflows. For instance, AI-enabled tools capable of transcribing and summarizing physician–patient interactions significantly alleviate administrative burdens, thereby enabling clinicians to allocate greater attention to direct patient care. This reallocation of resources fosters improvements in both organizational efficiency and patient-centered outcomes, reinforcing the strategic value of AI integration within healthcare systems.[ 5 , 6 ] Within the domain of predictive healthcare, AI models leverage both historical and real-time patient data to forecast potential health risks prior to their clinical manifestation. This anticipatory paradigm enables the formulation of individualized treatment strategies and the implementation of timely interventions, offering substantial benefits in the management of chronic conditions and the reduction of hospital readmissions. By shifting from reactive to proactive care delivery, AI-driven predictive analytics represent a critical advancement in precision medicine and healthcare sustainability.[ 7 ] Ocular oncology, a specialized branch of ophthalmology dedicated to the diagnosis and treatment of ocular tumors, has historically relied on clinical examinations for detecting intraocular and ocular surface malignancies, with histopathological confirmation primarily applied to surface-level cancers. A notable example is uveal melanoma—the most prevalent intraocular neoplasm—despite its relatively low incidence of approximately 0.1 per 100,000 individuals. Although complications are uncommon, optic neuropathy may arise following radiosurgical interventions, particularly when lesions are situated near the optic disc and optic nerve. Among established therapeutic modalities, linear accelerator based stereotactic radiosurgery remains a widely adopted approach, delivering a single dose under mechanical immobilization for small- to mid-stage uveal melanomas. Historically, diagnostic practices for intraocular malignancies have been grounded in clinical criteria that often lack sufficient precision. Addressing these limitations, AI has emerged as a transformative force, not only within healthcare but also across diverse sectors such as finance, commerce, and transportation. A comprehensive understanding of AI’s operational principles is essential to fully appreciate its potential in ocular oncology and beyond. Through iterative data processing, AI systems progressively enhance predictive accuracy and diagnostic reliability. Unlike human cognition, AI possesses the capacity to execute millions of computations continuously, conferring a distinct advantage in managing complex tasks and large-scale data analytics. [ 8 – 11 ] Contemporary healthcare systems increasingly depend on computational technologies to augment human decision-making, streamline data management, and support clinical processes. AI serves as a critical enabler of this transformation by synthesizing large-scale medical trends, assessing and quantifying risk factors, and generating predictive insights derived from complex datasets. Given the inherently data-intensive nature of healthcare, advanced analytics have become indispensable for improving operational efficiency and optimizing patient outcomes. A defining characteristic of this evolution is the exponential expansion in both the diversity and volume of medical data collected, encompassing genomic and behavioral profiles alongside clinical and environmental parameters. [ 12 , 13 ] The healthcare sector generates immense volumes of data daily, sourced from Internet of Things (IoT) devices for continuous patient monitoring, high-resolution imaging modalities, genomic sequencing platforms, and electronic health records. While these technological innovations have expanded the scope of data-driven care, healthcare management acknowledges persistent challenges that must be addressed. Critical concerns include data privacy, security, and the ethical governance of information, all of which are essential to ensuring that the benefits of AI are equitably distributed across diverse patient populations. To uphold public trust and safeguard patient welfare, transparent development practices and robust regulatory frameworks are imperative components of responsible AI integration in healthcare.[ 12 , 14 , 15 ] As AI continues to advance, healthcare management bears a critical responsibility to orchestrate its integration in a manner that ensures greater accuracy, efficiency, and accessibility in medical services. These technological innovations are establishing the foundation for a paradigm shift toward patient-centered care—one in which digital tools augment, rather than supplant, the human dimension of clinical practice. [ 7 , 16 , 17 ] AI represents one of the most versatile and transformative technologies driving contemporary business strategies aimed at delivering superior client services. Widely acknowledged as a disruptive force, AI is redefining operational paradigms across multiple industries. Within the broader context of digital transformation, a nuanced understanding of the challenges inherent in implementing advanced IT solutions is critical for optimizing organizational strategies and business models. Despite the substantial promise of these innovations, many institutions encounter persistent barriers to seamless integration into routine workflows. In healthcare, managerial leadership recognizes that while notable progress has been achieved, significant impediments remain. Key concerns include ethical governance, elevated implementation costs, and the complexity of regulatory compliance—all of which must be addressed to ensure safe and equitable deployment of AI technologies. Looking ahead, robotics is anticipated to assume an increasingly pivotal role in advancing personalized medicine, enabling remote healthcare delivery, and fostering global health equity. [ 18 – 20 ] Among the most impactful applications of AI in contemporary healthcare is its integration into medical diagnostics. Nowadays, advanced algorithmic models demonstrate the capacity to analyze and interpret highly complex datasets—including radiological imaging, laboratory parameters, and genomic profiles—with remarkable precision. These AI-driven diagnostic systems augment clinical decision-making by enabling earlier and more accurate detection of pathologies such as oncological, cardiovascular, and neurodegenerative disorders, frequently outperforming conventional diagnostic methodologies in terms of reliability and accuracy, reusing on top of standard two-dimensional (2D) images the three-dimensional (3D) imaging for augmented reality (AR) and/or virtual reality (VR) platforms. [ 14 , 21 , 22 ] Contemporary hospital management increasingly leverages agentic AI to optimize operational workflows by automating routine administrative processes, including patient intake, appointment scheduling, and insurance preauthorization. These automation strategies alleviate clinician workload, mitigate professional burnout, and enable healthcare providers to allocate greater time to direct patient care. Furthermore, several institutions have deployed autonomous AI agents to conduct post-discharge follow-ups, thereby enhancing treatment adherence and reducing hospital readmission rates. [ 15 , 23 , 24 ] To enable these advancements, the integration of AI-optimized databases—engineered to manage heterogeneous data types and deploy machine learning models—with an Enterprise Architecture (EA) framework has become increasingly critical. Leveraging EA methodologies, particularly through the ArchiMate modeling language, facilitates structured system design across business, application, and technology layers. This synergistic approach not only strengthens healthcare digital transformation initiatives but is also gaining traction across diverse research domains and industrial sectors as a means to enhance interoperability, scalability, and strategic alignment in complex digital ecosystems. [ 25 – 27 ] This research aims to examine how the strategic convergence of agentic AI and the EA framework can substantially advance managerial practices within the healthcare sector, with a specific focus on ocular oncology and radiology. In the context of accelerated digital transformation in medicine, the imperative for intelligent systems that facilitate evidence-based decision-making, optimize operational workflows, and enhance patient outcomes has become increasingly pronounced. [ 25 , 27 – 29 ] AI technologies deliver advanced capabilities in data analytics, diagnostic precision, and workflow automation, while the EA framework—particularly through modeling standards such as ArchiMate—offers a systematic approach to harmonizing business processes, application ecosystems, and technological infrastructure. The integration of these two paradigms enables healthcare organizations to effectively navigate complex operational landscapes, prioritize high-impact use cases, and deploy scalable solutions tailored to the specific challenges inherent in ocular oncology. [ 26 , 29 , 30 ] This study seeks to illustrate how a synergistic integration of AI and EA not only accelerates technological innovation but also fosters sustainable healthcare management by enhancing operational efficiency, reducing costs, and improving the overall quality of care. Viewed through this strategic lens, the research advances understanding of how digital technologies can be effectively leveraged to transform specialized medical domains and align with long-term organizational objectives. Methods and Material The authors adopted The Open Group Architecture Framework (TOGAF) standards to guide EA-based approaches for use case identification within the context of a Design Science Research (DSR) methodology. [ 31 – 33 ] This structured process enabled the establishment of prerequisites for healthcare management, facilitating the identification of both structural and functional components across relevant domains. The interdisciplinary scope encompassed multiple processes associated with AI-driven applications. Subsequent analysis of the focal area was conducted to ensure alignment with the strategic objectives and operational requirements of healthcare management, recognizing that AI-based initiatives entail substantial costs from initial development through full-scale deployment. To enable a decision-oriented data analytics strategy, the proposed methodology facilitates the systematic assessment of healthcare management requirements at the EA level through an Information and Communication Technologies (ICT) perspective. This approach supports the mapping of functional components and architectural building blocks to generic use cases. By employing a qualitative research design, the study seeks to validate the necessity of adopting AI-based solutions to achieve the strategic objectives and overarching vision of healthcare management. Results Drawing on the findings of the study, the authors identified a set of generic use cases by analyzing ocular oncology workflows through the combined perspectives of ICT and the TOGAF-based EA framework. Among these, a particularly significant use cases involved the integration of specialized applications enhanced with AI functionalities. While the research initially explored multiple use cases within ophthalmology, the focus ultimately converged to three examples in ocular oncology due to its inherent complexity and high potential for technological innovation. To substantiate these findings, the study established a structured mapping of essential ICT building blocks and functional components to the predefined relationships between healthcare management requirements and the identified use cases. This systematic alignment provides a robust foundation for integrating technological solutions with clinical and operational priorities. The overarching objective of the proposed AI-driven initiative is to strengthen healthcare management capabilities in addressing the distinctive challenges of ocular oncology, including time-intensive diagnostic procedures and workflow inefficiencies. To achieve this, the project envisions embedding a dedicated AI-powered module or AI agents within existing health information systems. This AI-powered module or special AI agents would enable automated data entry for constructing comprehensive patient medical histories, perform anatomical analyses of the globe and orbital structures, support diagnostic processes for ocular pathologies, and generate evidence-based therapeutic recommendations. The strategic vision for healthcare management emphasizes the deployment of an AI-enhanced application as an intuitive extension of existing health information systems. This AI-powered module or AI agents, for example, purpose-built for ocular oncology, is designed to facilitate automated documentation, advanced clinical data analysis, disease detection, and therapy planning. This structured framework not only enables the practical implementation of AI within ocular oncology but also advances the overarching agenda of digital transformation in healthcare. In vision of deploying enhanced imaging technologies, the AI agents can be embedded into the 3D based image systems supporting the usage of the capability of AR platform during the patient’s presence, and VR platform using the patient’s stored data for remote or later diagnosis verification that can be expanded for education purposes. The study delineated several high-priorities of specific use cases within ocular oncology that is systematically aligned with defined healthcare management requirements and supported by the AI-enabled architectural components. These structured mappings can demonstrate how domain-specific applications drive improvements in operational efficiency, enhance data integrity, and strengthen evidence-based clinical decision-making. The decision making supported by the AR and VR platforms visionary can facilitate the speed up of the outcomes either directly during the patient’s investigation or later using the patient’s stored image data. The simplified mapping of identified use cases to healthcare requirements and AI-building blocks in ocular oncology with one example focusing to radiosurgery is shown in the Table 1 . In the radiosurgery, the primary objective is to minimize the time required for delineating critical anatomical structures—such as the optic nerves, optic chiasm, brainstem, and anterior segment—while reducing interobserver variability in the contouring process. For single-session irradiation, rigid-frame-based fixation is employed, with ocular immobilization achieved through mechanical attachment of the extraocular muscles to the stereotactic frame. Following the fusion of computed tomography and magnetic resonance imaging, essential structures—including the optic nerves, chiasm, brainstem, cochlea, and lens—are delineated either by AI algorithms or by a radiation oncologist. Although AI-generated treatment plans demonstrate potential for significant time savings compared to manual contouring, physician review and adjustments remain indispensable to ensure clinical accuracy (Fig. 1 , Fig. 2 ). Table 1 AI-Enabled Capabilities in Ophthalmic Care: Requirements, Process Area, Mapped Requirements, AI-Components, and Target Outcomes # Capability Process Area Mapped Requirements AI Components Target Outcome 1 Automated Composition of Medical History from Clinical Encounters Data Capture Improve quality and relevance of patient information; optimize human resource utilization; enhance business process efficiency NLP; ASR AI-driven data entry for medical history creation, reducing manual workload and improving data accuracy 2 Aggregation and Harmonization of Longitudinal Patient Records Data Integration Enhance clinical data quality and relevance; improve IT system usability NLP Unified medical history through integration of multi-source patient data within an AI-enhanced application 3 Automated Analysis of Ocular Globe and Orbital Imaging Image Analysis Improve diagnostic data quality and relevance; increase IT system usability IRaI AI-supported assessment of ocular images to assist clinical evaluations via a dedicated module, using also the capability of AR during the patient’s presence or VR using the stored data 4 AI-Assisted Diagnostic Classification of Ocular Diseases Diagnosis Improve diagnostic accuracy and relevance; enhance IT usability IRaI; NLP Integrated analysis of visual and textual data to enable AI-assisted diagnosis of ocular conditions using also the capability of AR during the patient’s presence or VR using the stored data 5 AI-Driven Therapeutic Recommendation for Ocular Disorders Treatment Decision Support Increase business process efficiency; improve IT usability IRaI; NPL Generation of AI-supported treatment recommendations based on diagnostic inputs, streamlining decision-making, using also the capability of AR during the patient’s presence or VR using the stored data 6 Automated Stereotactic Radiotherapy Planning in Ocular Oncology Treatment Planning Improve data quality and relevance; enhance process efficiency; optimize IT usability IRaI AI-enabled stereotactic planning to improve precision and reduce planning time, using also the capability of AR during the patient’s presence or VR using the stored data Abbreviations: NLP = Natural Language Processing; ASR = Automatic Speech Recognition; IRaI = Image Recognition and Interpretation; VR = Virtual Reality; AR = Augmented Reality Table 1 . AI-Enabled Capabilities in Ophthalmic Care: Requirements, Process Area, Mapped Requirements, AI-Components, and Target Outcomes Discussion AI-driven optimization holds considerable potential for advancing patient-specific solutions in healthcare by improving constraint satisfaction, thereby enhancing both performance and safety outcomes. Although deep learning has emerged as the predominant technique across numerous ophthalmology subspecialties, classical machine learning approaches continue to play a critical role in ocular oncology research. This persistence is primarily attributable to the limited availability of high-quality imaging datasets and the relatively low incidence of ocular tumors, which constrains the feasibility of training robust deep learning models. Within the context of uveal melanoma, prognostic modeling remains among the most prevalent AI applications. A promising trajectory for future research involves leveraging deep learning to analyze digital pathology images, preferable of high-quality, offering novel opportunities for precise diagnosis and risk stratification in ocular oncology where AI agents can facilitate the navigation in 3D computer-assisted diagnosis. [ 8 , 34 – 36 ] This study deliberately avoids prescribing specific AI methodologies, such as machine learning or deep learning, due to the dynamic and rapidly evolving nature of AI technologies. Techniques are frequently adapted or superseded in response to emerging research and application-specific requirements. For example, while deep learning has gained prominence across numerous domains, classical machine learning approaches continue to hold relevance in ocular oncology, particularly in prognostic modeling for conditions such as uveal melanoma. Adopting a management-oriented perspective, this research prioritizes the identification of use cases, strategic objectives, and anticipated outcomes. This approach ensures that the findings maintain broad applicability and are not constrained by the transient popularity or inherent limitations of any single AI technique. The integration of AI into healthcare is increasingly acknowledged for its capacity to optimize patient-specific solutions through advanced constraint satisfaction techniques, thereby improving both clinical performance and safety outcomes. Although deep learning has emerged as the predominant methodology across numerous ophthalmic subspecialties, classical machine learning algorithms remain widely utilized in ocular oncology research. This continued relevance is primarily due to the scarcity of high-quality imaging datasets and the limited number of documented cases, which constrain the feasibility of training robust deep learning models. In the context of uveal melanoma—the most common primary intraocular malignancy in adults—AI applications frequently focus on prognostic modeling. A particularly promising direction involves leveraging deep learning for the analysis of digital pathology images, opening new possibilities for precise diagnosis and risk stratification in ocular oncology. [ 16 , 37 , 38 ] As the escalation of healthcare expenditures continues to surpass overall economic growth, this study shows that it has become imperative for healthcare administrators to assess AI initiatives through a rigorous financial perspective. Prior to implementation, decision-makers should prioritize identifying use cases that demonstrate the highest potential for generating organizational value. Furthermore, establishing precise project specifications and articulating clear objectives aligned with strategic priorities is critical. In the absence of such clarity, institutions risk allocating resources to initiatives that fail to produce substantive outcomes, resulting in financial inefficiencies and operational setbacks. Adopting a systematic, value-oriented framework for AI integration can facilitate optimal resource utilization and foster innovations that drive sustainable enhancements in healthcare delivery. As we describe, the increasing integration of AI into ophthalmology research underscores the transformative impact of computational technologies on contemporary medical practice. Notably, investigations targeting ocular surface disorders have accelerated, propelled by AI’s potential to address diagnostic complexities arising from the heterogeneous and multimodal nature of ocular imaging. These conditions frequently necessitate the use of multiple imaging modalities, which can hinder consistent interpretation and clinical decision-making. Although current AI models face developmental constraints, their emerging capabilities hold significant promise for enhancing objectivity in diagnosis and enabling data-driven treatment planning. [ 39 – 41 ] AI algorithms are increasingly leveraged to extract and interpret vast quantities of nuanced, previously imperceptible information embedded within ophthalmic imaging data. Recent technological advancements are empowering researchers to gain deeper insights into the pathophysiology of ocular surface diseases, facilitate the identification of clinically significant biomarkers, and explore novel therapeutic approaches. Due to the inherently interdisciplinary nature of AI applications, effective collaboration among clinicians, data scientists, and engineers is critical for translating these advancements into practical, patient-centered tools. Despite persistent challenges such as variability in data quality and the need for greater model interpretability, AI is positioned to exert a transformative influence on the future landscape of ophthalmic medicine. [ 41 – 43 ] Recent evidence underscores that an interdisciplinary approach constitutes a critical success factor in the implementation of AI-driven initiatives within healthcare supporting the healthcare management vision. This trend accentuates the increasing need for robust project management competencies across healthcare organizations. Coordinated efforts spanning clinical, technical, and administrative domains are indispensable to ensure that AI projects align with strategic objectives, receive adequate resource allocation, and integrate seamlessly into established workflows. As AI continues to redefine healthcare paradigms, the capacity to manage complex, cross-functional projects will be pivotal in unlocking its full transformative potential. AI is increasingly recognized as a transformative force across medical research, education, and clinical practice. In medical education and preoperative training often the 3D printing is used. Nowadays, AI facilitates adaptive learning pathways and immersive simulation environments, while in clinical contexts, AI supports advanced diagnostic processes. Beyond these applications, AI demonstrates considerable potential for population-level screening and individualized therapeutic decision-making. Diabetic retinopathy (DR), a prevalent microvascular complication of diabetes and a leading cause of vision impairment worldwide, exemplifies an area where AI-based deployments can deliver substantial impact. Early detection of DR is critical to prevent progression to sight-threatening stages and irreversible visual loss. Consequently, systematic screening programs have been adopted in numerous healthcare systems, given their proven cost-effectiveness in reducing blindness risk. AI-driven diagnostic platforms, leveraging digital fundus photography and optical coherence tomography, aim to improve accuracy and efficiency in DR detection and grading. These technologies offer the possibility of early-stage identification without dependence on specialized clinical infrastructure, thereby expanding access to care. High-performance algorithms supported by AI techniques with robust sensitivity and specificity enable timely clinical decisions and facilitate real-time monitoring for ophthalmic and endocrine interventions. Emerging modalities, such as handheld imaging devices, present opportunities for patient self-monitoring; however, limitations in image quality currently constrain their clinical utility. Integration of such tools into telemedicine frameworks could allow remote image acquisition and transmission to centralized reading centers for expert interpretation and treatment planning. The overarching objective remains the development of automated, scalable AI systems capable of delivering rapid, reliable DR screening and severity assessment, ultimately optimizing patient outcomes while reducing resource demands. [ 44 – 48 ] Our approach, guided by EA principles, seeks to enhance diagnostic capabilities by extending current visualization techniques beyond conventional 2D imaging. The objective is to integrate advanced 3D modalities, such as combined magnetic resonance imaging and computed tomography, into diagnostic and educational workflows. These 3D imaging solutions can be deployed alongside AR, including 2D images, and VR platforms, enabling immersive spatial navigation and interpretation. By leveraging AI agents within this framework, clinicians can explore anatomical structures in 3D, facilitating precise identification of critical points and regions. This methodology not only supports the detection of DR indicators but also holds significant potential for identifying oncological pathologies, thereby broadening the scope of AI-assisted diagnostics, including education. In addition, the use of composable AI agents may in parallel diagnose and point to more diseases. If compare to 3D printing, the vision of VR platform usage can bring significant savings in the long term and is definitely more environmental friendly. As financial pressures intensify within healthcare systems, the integration of AI technologies is emerging as a strategic imperative rather than a discretionary innovation. With operational budgets contracting and expenditures escalating at a rate that surpasses economic growth, healthcare providers increasingly leverage AI not only to drive technological advancement but also to achieve cost optimization. Consequently, the implementation of AI methodologies in real-world healthcare settings has become both logical and necessary. These solutions hold significant potential to streamline workflows, minimize administrative overhead, and enhance service delivery—positioning AI as a critical enabler for addressing clinical and operational challenges under stringent financial constraints. Conclusion The incorporation of AI into healthcare operations offers significant potential for enhancing efficiency and accelerating clinical decision-making. As digital transformation advances, AI-driven applications are creating new opportunities to improve both the quality and delivery of healthcare services. Among the most notable benefits is the reduction of turnaround times within clinical workflows, which directly contributes to improved patient outcomes and satisfaction. By automating routine processes—such as data entry, diagnostic evaluations, and follow-up communications—AI allows clinicians to allocate more time to direct patient care, thereby reducing administrative burdens and enhancing overall clinical efficiency. This transition not only optimizes operational performance but also mitigates workload pressures on healthcare professionals. Contemporary AI systems have evolved to perform complex functions traditionally requiring human expertise, including image recognition and interpretation including the by AI agent-based supported navigation using AR and VR platforms, natural language processing, and automatic speech recognition. These capabilities can be integrated into advanced platforms that simultaneously analyze heterogeneous data sources—such as medical imaging, clinical documentation, and verbal interactions—providing real-time insights to support accurate diagnosis and personalized treatment planning. In ocular oncology, such innovations are particularly impactful, facilitating precise evaluation of ocular conditions and streamlining the management of intricate cases. Ultimately, the adoption of AI within healthcare is driving a paradigm shift toward a more responsive, data-centric, and patient-focused model of care. By augmenting, replacing or enhancing manual processes with intelligent systems, the management of healthcare organizations can prove the higher level and significant costs saving in healthcare service delivery. Declarations Conflict of interest The authors declare no competing interests Funding Open access funding provided by The Ministry of Education, Science, Research and Sport of the Slovak Republic in cooperation with Centre for Scientific and Technical Information of the Slovak Republic. Author Contribution R.F., P.V., J.V., P.K. and A.F. wrote the main manuscript text. R.F. and M.G. prepared the table. R.F. and M.C. prepared the picture. 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Jan, 2026 Reviews received at journal 10 Jan, 2026 Reviews received at journal 24 Dec, 2025 Reviewers agreed at journal 24 Dec, 2025 Reviewers agreed at journal 22 Dec, 2025 Reviewers invited by journal 16 Dec, 2025 Editor assigned by journal 15 Dec, 2025 Submission checks completed at journal 15 Dec, 2025 First submitted to journal 13 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8354984","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":562228879,"identity":"312c29ef-ea25-4930-958b-641a0dd32118","order_by":0,"name":"Robert 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1","display":"","copyAsset":false,"role":"figure","size":638117,"visible":true,"origin":"","legend":"\u003cp\u003eMain panel: Individual stereotactic radiosurgical treatment planning scheme for the patient with uveal melanoma, with a prescribed therapeutic dose (TD) of 40.3 Gy delivered to the melanoma target. Lower right panel: Ultrasonographic imaging (A-scan and B-scan) of the intraocular melanoma prior to stereotactic irradiation. (Source: Authors’ institution.)\u003c/p\u003e","description":"","filename":"Figure1rf.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8354984/v1/a5c7b6a1b6759f2b62897d20.jpg"},{"id":98775248,"identity":"77a9796e-1338-4298-84f4-827aa59da328","added_by":"auto","created_at":"2025-12-22 12:18:59","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1593211,"visible":true,"origin":"","legend":"\u003cp\u003e3D model schematic representations of a patient with intraocular melanoma undergoing stereotactic irradiation using a linear accelerator, supported by AI for treatment planning. (Source: Authors’ institution.)\u003c/p\u003e","description":"","filename":"Figure2rf.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8354984/v1/a777452ee1b7715b6bfdb2eb.jpg"},{"id":98782729,"identity":"670f006d-3a2e-48a1-9a25-07aacd05f926","added_by":"auto","created_at":"2025-12-22 12:40:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2755387,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8354984/v1/a1eeba92-586a-400c-82d8-ff9297620a1e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Strategic Perspectives on Artificial Intelligence Applications in Ocular Oncology: Managerial Insights and General Use Cases","fulltext":[{"header":"Introduction","content":"\u003cp\u003eArtificial Intelligence (AI) has evolved from a conceptual framework introduced in 1956 into a transformative technological paradigm that permeates contemporary discourse. Despite its ubiquity, the inherent complexity of AI remains opaque to many stakeholders. Analogous to the multidimensional nature of human cognition described in psychology, AI encompasses a diverse array of computational and algorithmic components. This overview delineates the historical trajectory of AI without presupposing expertise in computer science or philosophy, emphasizing its current role as a catalyst for innovation in medicine. Far beyond its origins in academic research, AI now actively supports healthcare professionals by enhancing diagnostic accuracy, improving clinical outcomes, and optimizing systemic efficiency on a global scale. Crucially, healthcare management assumes a pivotal function in steering these advancements through evidence-based decision-making and strategic governance.[\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eHealthcare management assumes a critical role in ensuring that AI technologies contribute not only to clinical decision-making but also to the optimization of operational workflows. For instance, AI-enabled tools capable of transcribing and summarizing physician\u0026ndash;patient interactions significantly alleviate administrative burdens, thereby enabling clinicians to allocate greater attention to direct patient care. This reallocation of resources fosters improvements in both organizational efficiency and patient-centered outcomes, reinforcing the strategic value of AI integration within healthcare systems.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eWithin the domain of predictive healthcare, AI models leverage both historical and real-time patient data to forecast potential health risks prior to their clinical manifestation. This anticipatory paradigm enables the formulation of individualized treatment strategies and the implementation of timely interventions, offering substantial benefits in the management of chronic conditions and the reduction of hospital readmissions. By shifting from reactive to proactive care delivery, AI-driven predictive analytics represent a critical advancement in precision medicine and healthcare sustainability.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eOcular oncology, a specialized branch of ophthalmology dedicated to the diagnosis and treatment of ocular tumors, has historically relied on clinical examinations for detecting intraocular and ocular surface malignancies, with histopathological confirmation primarily applied to surface-level cancers. A notable example is uveal melanoma\u0026mdash;the most prevalent intraocular neoplasm\u0026mdash;despite its relatively low incidence of approximately 0.1 per 100,000 individuals. Although complications are uncommon, optic neuropathy may arise following radiosurgical interventions, particularly when lesions are situated near the optic disc and optic nerve. Among established therapeutic modalities, linear accelerator based stereotactic radiosurgery remains a widely adopted approach, delivering a single dose under mechanical immobilization for small- to mid-stage uveal melanomas. Historically, diagnostic practices for intraocular malignancies have been grounded in clinical criteria that often lack sufficient precision. Addressing these limitations, AI has emerged as a transformative force, not only within healthcare but also across diverse sectors such as finance, commerce, and transportation. A comprehensive understanding of AI\u0026rsquo;s operational principles is essential to fully appreciate its potential in ocular oncology and beyond. Through iterative data processing, AI systems progressively enhance predictive accuracy and diagnostic reliability. Unlike human cognition, AI possesses the capacity to execute millions of computations continuously, conferring a distinct advantage in managing complex tasks and large-scale data analytics. [\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eContemporary healthcare systems increasingly depend on computational technologies to augment human decision-making, streamline data management, and support clinical processes. AI serves as a critical enabler of this transformation by synthesizing large-scale medical trends, assessing and quantifying risk factors, and generating predictive insights derived from complex datasets. Given the inherently data-intensive nature of healthcare, advanced analytics have become indispensable for improving operational efficiency and optimizing patient outcomes. A defining characteristic of this evolution is the exponential expansion in both the diversity and volume of medical data collected, encompassing genomic and behavioral profiles alongside clinical and environmental parameters. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe healthcare sector generates immense volumes of data daily, sourced from Internet of Things (IoT) devices for continuous patient monitoring, high-resolution imaging modalities, genomic sequencing platforms, and electronic health records. While these technological innovations have expanded the scope of data-driven care, healthcare management acknowledges persistent challenges that must be addressed. Critical concerns include data privacy, security, and the ethical governance of information, all of which are essential to ensuring that the benefits of AI are equitably distributed across diverse patient populations. To uphold public trust and safeguard patient welfare, transparent development practices and robust regulatory frameworks are imperative components of responsible AI integration in healthcare.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAs AI continues to advance, healthcare management bears a critical responsibility to orchestrate its integration in a manner that ensures greater accuracy, efficiency, and accessibility in medical services. These technological innovations are establishing the foundation for a paradigm shift toward patient-centered care\u0026mdash;one in which digital tools augment, rather than supplant, the human dimension of clinical practice. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAI represents one of the most versatile and transformative technologies driving contemporary business strategies aimed at delivering superior client services. Widely acknowledged as a disruptive force, AI is redefining operational paradigms across multiple industries. Within the broader context of digital transformation, a nuanced understanding of the challenges inherent in implementing advanced IT solutions is critical for optimizing organizational strategies and business models. Despite the substantial promise of these innovations, many institutions encounter persistent barriers to seamless integration into routine workflows. In healthcare, managerial leadership recognizes that while notable progress has been achieved, significant impediments remain. Key concerns include ethical governance, elevated implementation costs, and the complexity of regulatory compliance\u0026mdash;all of which must be addressed to ensure safe and equitable deployment of AI technologies. Looking ahead, robotics is anticipated to assume an increasingly pivotal role in advancing personalized medicine, enabling remote healthcare delivery, and fostering global health equity. [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAmong the most impactful applications of AI in contemporary healthcare is its integration into medical diagnostics. Nowadays, advanced algorithmic models demonstrate the capacity to analyze and interpret highly complex datasets\u0026mdash;including radiological imaging, laboratory parameters, and genomic profiles\u0026mdash;with remarkable precision. These AI-driven diagnostic systems augment clinical decision-making by enabling earlier and more accurate detection of pathologies such as oncological, cardiovascular, and neurodegenerative disorders, frequently outperforming conventional diagnostic methodologies in terms of reliability and accuracy, reusing on top of standard two-dimensional (2D) images the three-dimensional (3D) imaging for augmented reality (AR) and/or virtual reality (VR) platforms. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eContemporary hospital management increasingly leverages agentic AI to optimize operational workflows by automating routine administrative processes, including patient intake, appointment scheduling, and insurance preauthorization. These automation strategies alleviate clinician workload, mitigate professional burnout, and enable healthcare providers to allocate greater time to direct patient care. Furthermore, several institutions have deployed autonomous AI agents to conduct post-discharge follow-ups, thereby enhancing treatment adherence and reducing hospital readmission rates. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eTo enable these advancements, the integration of AI-optimized databases\u0026mdash;engineered to manage heterogeneous data types and deploy machine learning models\u0026mdash;with an Enterprise Architecture (EA) framework has become increasingly critical. Leveraging EA methodologies, particularly through the ArchiMate modeling language, facilitates structured system design across business, application, and technology layers. This synergistic approach not only strengthens healthcare digital transformation initiatives but is also gaining traction across diverse research domains and industrial sectors as a means to enhance interoperability, scalability, and strategic alignment in complex digital ecosystems. [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThis research aims to examine how the strategic convergence of agentic AI and the EA framework can substantially advance managerial practices within the healthcare sector, with a specific focus on ocular oncology and radiology. In the context of accelerated digital transformation in medicine, the imperative for intelligent systems that facilitate evidence-based decision-making, optimize operational workflows, and enhance patient outcomes has become increasingly pronounced. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAI technologies deliver advanced capabilities in data analytics, diagnostic precision, and workflow automation, while the EA framework\u0026mdash;particularly through modeling standards such as ArchiMate\u0026mdash;offers a systematic approach to harmonizing business processes, application ecosystems, and technological infrastructure. The integration of these two paradigms enables healthcare organizations to effectively navigate complex operational landscapes, prioritize high-impact use cases, and deploy scalable solutions tailored to the specific challenges inherent in ocular oncology. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThis study seeks to illustrate how a synergistic integration of AI and EA not only accelerates technological innovation but also fosters sustainable healthcare management by enhancing operational efficiency, reducing costs, and improving the overall quality of care. Viewed through this strategic lens, the research advances understanding of how digital technologies can be effectively leveraged to transform specialized medical domains and align with long-term organizational objectives.\u003c/p\u003e"},{"header":"Methods and Material","content":"\u003cp\u003eThe authors adopted The Open Group Architecture Framework (TOGAF) standards to guide EA-based approaches for use case identification within the context of a Design Science Research (DSR) methodology. [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] This structured process enabled the establishment of prerequisites for healthcare management, facilitating the identification of both structural and functional components across relevant domains. The interdisciplinary scope encompassed multiple processes associated with AI-driven applications. Subsequent analysis of the focal area was conducted to ensure alignment with the strategic objectives and operational requirements of healthcare management, recognizing that AI-based initiatives entail substantial costs from initial development through full-scale deployment.\u003c/p\u003e \u003cp\u003eTo enable a decision-oriented data analytics strategy, the proposed methodology facilitates the systematic assessment of healthcare management requirements at the EA level through an Information and Communication Technologies (ICT) perspective. This approach supports the mapping of functional components and architectural building blocks to generic use cases. By employing a qualitative research design, the study seeks to validate the necessity of adopting AI-based solutions to achieve the strategic objectives and overarching vision of healthcare management.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDrawing on the findings of the study, the authors identified a set of generic use cases by analyzing ocular oncology workflows through the combined perspectives of ICT and the TOGAF-based EA framework. Among these, a particularly significant use cases involved the integration of specialized applications enhanced with AI functionalities. While the research initially explored multiple use cases within ophthalmology, the focus ultimately converged to three examples in ocular oncology due to its inherent complexity and high potential for technological innovation.\u003c/p\u003e \u003cp\u003eTo substantiate these findings, the study established a structured mapping of essential ICT building blocks and functional components to the predefined relationships between healthcare management requirements and the identified use cases. This systematic alignment provides a robust foundation for integrating technological solutions with clinical and operational priorities. The overarching objective of the proposed AI-driven initiative is to strengthen healthcare management capabilities in addressing the distinctive challenges of ocular oncology, including time-intensive diagnostic procedures and workflow inefficiencies. To achieve this, the project envisions embedding a dedicated AI-powered module or AI agents within existing health information systems. This AI-powered module or special AI agents would enable automated data entry for constructing comprehensive patient medical histories, perform anatomical analyses of the globe and orbital structures, support diagnostic processes for ocular pathologies, and generate evidence-based therapeutic recommendations.\u003c/p\u003e \u003cp\u003eThe strategic vision for healthcare management emphasizes the deployment of an AI-enhanced application as an intuitive extension of existing health information systems. This AI-powered module or AI agents, for example, purpose-built for ocular oncology, is designed to facilitate automated documentation, advanced clinical data analysis, disease detection, and therapy planning. This structured framework not only enables the practical implementation of AI within ocular oncology but also advances the overarching agenda of digital transformation in healthcare. In vision of deploying enhanced imaging technologies, the AI agents can be embedded into the 3D based image systems supporting the usage of the capability of AR platform during the patient\u0026rsquo;s presence, and VR platform using the patient\u0026rsquo;s stored data for remote or later diagnosis verification that can be expanded for education purposes.\u003c/p\u003e \u003cp\u003eThe study delineated several high-priorities of specific use cases within ocular oncology that is systematically aligned with defined healthcare management requirements and supported by the AI-enabled architectural components. These structured mappings can demonstrate how domain-specific applications drive improvements in operational efficiency, enhance data integrity, and strengthen evidence-based clinical decision-making. The decision making supported by the AR and VR platforms visionary can facilitate the speed up of the outcomes either directly during the patient\u0026rsquo;s investigation or later using the patient\u0026rsquo;s stored image data. The simplified mapping of identified use cases to healthcare requirements and AI-building blocks in ocular oncology with one example focusing to radiosurgery is shown in the Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In the radiosurgery, the primary objective is to minimize the time required for delineating critical anatomical structures\u0026mdash;such as the optic nerves, optic chiasm, brainstem, and anterior segment\u0026mdash;while reducing interobserver variability in the contouring process. For single-session irradiation, rigid-frame-based fixation is employed, with ocular immobilization achieved through mechanical attachment of the extraocular muscles to the stereotactic frame. Following the fusion of computed tomography and magnetic resonance imaging, essential structures\u0026mdash;including the optic nerves, chiasm, brainstem, cochlea, and lens\u0026mdash;are delineated either by AI algorithms or by a radiation oncologist. Although AI-generated treatment plans demonstrate potential for significant time savings compared to manual contouring, physician review and adjustments remain indispensable to ensure clinical accuracy (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAI-Enabled Capabilities in Ophthalmic Care: Requirements, Process Area, Mapped Requirements, AI-Components, and Target Outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e#\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCapability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProcess Area\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMapped Requirements\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAI Components\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTarget Outcome\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAutomated Composition of Medical History from Clinical Encounters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData Capture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eImprove quality and relevance of patient information; optimize human resource utilization; enhance business process efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNLP; ASR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAI-driven data entry for medical history creation, reducing manual workload and improving data accuracy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAggregation and Harmonization of Longitudinal Patient Records\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData Integration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnhance clinical data quality and relevance; improve IT system usability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnified medical history through integration of multi-source patient data within an AI-enhanced application\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAutomated Analysis of Ocular Globe and Orbital Imaging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImage Analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eImprove diagnostic data quality and relevance; increase IT system usability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIRaI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAI-supported assessment of ocular images to assist clinical evaluations via a dedicated module, using also the capability of AR during the patient\u0026rsquo;s presence or VR using the stored data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI-Assisted Diagnostic Classification of Ocular Diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eImprove diagnostic accuracy and relevance; enhance IT usability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIRaI; NLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIntegrated analysis of visual and textual data to enable AI-assisted diagnosis of ocular conditions using also the capability of AR during the patient\u0026rsquo;s presence or VR using the stored data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI-Driven Therapeutic Recommendation for Ocular Disorders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTreatment Decision Support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncrease business process efficiency; improve IT usability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIRaI; NPL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGeneration of AI-supported treatment recommendations based on diagnostic inputs, streamlining decision-making, using also the capability of AR during the patient\u0026rsquo;s presence or VR using the stored data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAutomated Stereotactic Radiotherapy Planning in Ocular Oncology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTreatment Planning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eImprove data quality and relevance; enhance process efficiency; optimize IT usability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIRaI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAI-enabled stereotactic planning to improve precision and reduce planning time, using also the capability of AR during the patient\u0026rsquo;s presence or VR using the stored data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: NLP\u0026thinsp;=\u0026thinsp;Natural Language Processing; ASR\u0026thinsp;=\u0026thinsp;Automatic Speech Recognition; IRaI\u0026thinsp;=\u0026thinsp;Image Recognition and Interpretation; VR\u0026thinsp;=\u0026thinsp;Virtual Reality; AR\u0026thinsp;=\u0026thinsp;Augmented Reality\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. AI-Enabled Capabilities in Ophthalmic Care: Requirements, Process Area, Mapped Requirements, AI-Components, and Target Outcomes\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAI-driven optimization holds considerable potential for advancing patient-specific solutions in healthcare by improving constraint satisfaction, thereby enhancing both performance and safety outcomes. Although deep learning has emerged as the predominant technique across numerous ophthalmology subspecialties, classical machine learning approaches continue to play a critical role in ocular oncology research. This persistence is primarily attributable to the limited availability of high-quality imaging datasets and the relatively low incidence of ocular tumors, which constrains the feasibility of training robust deep learning models. Within the context of uveal melanoma, prognostic modeling remains among the most prevalent AI applications. A promising trajectory for future research involves leveraging deep learning to analyze digital pathology images, preferable of high-quality, offering novel opportunities for precise diagnosis and risk stratification in ocular oncology where AI agents can facilitate the navigation in 3D computer-assisted diagnosis. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e–\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] This study deliberately avoids prescribing specific AI methodologies, such as machine learning or deep learning, due to the dynamic and rapidly evolving nature of AI technologies. Techniques are frequently adapted or superseded in response to emerging research and application-specific requirements. For example, while deep learning has gained prominence across numerous domains, classical machine learning approaches continue to hold relevance in ocular oncology, particularly in prognostic modeling for conditions such as uveal melanoma. Adopting a management-oriented perspective, this research prioritizes the identification of use cases, strategic objectives, and anticipated outcomes. This approach ensures that the findings maintain broad applicability and are not constrained by the transient popularity or inherent limitations of any single AI technique.\u003c/p\u003e \u003cp\u003eThe integration of AI into healthcare is increasingly acknowledged for its capacity to optimize patient-specific solutions through advanced constraint satisfaction techniques, thereby improving both clinical performance and safety outcomes. Although deep learning has emerged as the predominant methodology across numerous ophthalmic subspecialties, classical machine learning algorithms remain widely utilized in ocular oncology research. This continued relevance is primarily due to the scarcity of high-quality imaging datasets and the limited number of documented cases, which constrain the feasibility of training robust deep learning models. In the context of uveal melanoma—the most common primary intraocular malignancy in adults—AI applications frequently focus on prognostic modeling. A particularly promising direction involves leveraging deep learning for the analysis of digital pathology images, opening new possibilities for precise diagnosis and risk stratification in ocular oncology. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] As the escalation of healthcare expenditures continues to surpass overall economic growth, this study shows that it has become imperative for healthcare administrators to assess AI initiatives through a rigorous financial perspective. Prior to implementation, decision-makers should prioritize identifying use cases that demonstrate the highest potential for generating organizational value. Furthermore, establishing precise project specifications and articulating clear objectives aligned with strategic priorities is critical. In the absence of such clarity, institutions risk allocating resources to initiatives that fail to produce substantive outcomes, resulting in financial inefficiencies and operational setbacks. Adopting a systematic, value-oriented framework for AI integration can facilitate optimal resource utilization and foster innovations that drive sustainable enhancements in healthcare delivery.\u003c/p\u003e \u003cp\u003eAs we describe, the increasing integration of AI into ophthalmology research underscores the transformative impact of computational technologies on contemporary medical practice. Notably, investigations targeting ocular surface disorders have accelerated, propelled by AI’s potential to address diagnostic complexities arising from the heterogeneous and multimodal nature of ocular imaging. These conditions frequently necessitate the use of multiple imaging modalities, which can hinder consistent interpretation and clinical decision-making. Although current AI models face developmental constraints, their emerging capabilities hold significant promise for enhancing objectivity in diagnosis and enabling data-driven treatment planning. [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e–\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAI algorithms are increasingly leveraged to extract and interpret vast quantities of nuanced, previously imperceptible information embedded within ophthalmic imaging data. Recent technological advancements are empowering researchers to gain deeper insights into the pathophysiology of ocular surface diseases, facilitate the identification of clinically significant biomarkers, and explore novel therapeutic approaches. Due to the inherently interdisciplinary nature of AI applications, effective collaboration among clinicians, data scientists, and engineers is critical for translating these advancements into practical, patient-centered tools. Despite persistent challenges such as variability in data quality and the need for greater model interpretability, AI is positioned to exert a transformative influence on the future landscape of ophthalmic medicine. [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e–\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] Recent evidence underscores that an interdisciplinary approach constitutes a critical success factor in the implementation of AI-driven initiatives within healthcare supporting the healthcare management vision. This trend accentuates the increasing need for robust project management competencies across healthcare organizations. Coordinated efforts spanning clinical, technical, and administrative domains are indispensable to ensure that AI projects align with strategic objectives, receive adequate resource allocation, and integrate seamlessly into established workflows. As AI continues to redefine healthcare paradigms, the capacity to manage complex, cross-functional projects will be pivotal in unlocking its full transformative potential.\u003c/p\u003e \u003cp\u003eAI is increasingly recognized as a transformative force across medical research, education, and clinical practice. In medical education and preoperative training often the 3D printing is used. Nowadays, AI facilitates adaptive learning pathways and immersive simulation environments, while in clinical contexts, AI supports advanced diagnostic processes. Beyond these applications, AI demonstrates considerable potential for population-level screening and individualized therapeutic decision-making. Diabetic retinopathy (DR), a prevalent microvascular complication of diabetes and a leading cause of vision impairment worldwide, exemplifies an area where AI-based deployments can deliver substantial impact. Early detection of DR is critical to prevent progression to sight-threatening stages and irreversible visual loss. Consequently, systematic screening programs have been adopted in numerous healthcare systems, given their proven cost-effectiveness in reducing blindness risk. AI-driven diagnostic platforms, leveraging digital fundus photography and optical coherence tomography, aim to improve accuracy and efficiency in DR detection and grading. These technologies offer the possibility of early-stage identification without dependence on specialized clinical infrastructure, thereby expanding access to care. High-performance algorithms supported by AI techniques with robust sensitivity and specificity enable timely clinical decisions and facilitate real-time monitoring for ophthalmic and endocrine interventions. Emerging modalities, such as handheld imaging devices, present opportunities for patient self-monitoring; however, limitations in image quality currently constrain their clinical utility. Integration of such tools into telemedicine frameworks could allow remote image acquisition and transmission to centralized reading centers for expert interpretation and treatment planning. The overarching objective remains the development of automated, scalable AI systems capable of delivering rapid, reliable DR screening and severity assessment, ultimately optimizing patient outcomes while reducing resource demands. [\u003cspan additionalcitationids=\"CR45 CR46 CR47\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e–\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] Our approach, guided by EA principles, seeks to enhance diagnostic capabilities by extending current visualization techniques beyond conventional 2D imaging. The objective is to integrate advanced 3D modalities, such as combined magnetic resonance imaging and computed tomography, into diagnostic and educational workflows. These 3D imaging solutions can be deployed alongside AR, including 2D images, and VR platforms, enabling immersive spatial navigation and interpretation. By leveraging AI agents within this framework, clinicians can explore anatomical structures in 3D, facilitating precise identification of critical points and regions. This methodology not only supports the detection of DR indicators but also holds significant potential for identifying oncological pathologies, thereby broadening the scope of AI-assisted diagnostics, including education. In addition, the use of composable AI agents may in parallel diagnose and point to more diseases. If compare to 3D printing, the vision of VR platform usage can bring significant savings in the long term and is definitely more environmental friendly.\u003c/p\u003e \u003cp\u003eAs financial pressures intensify within healthcare systems, the integration of AI technologies is emerging as a strategic imperative rather than a discretionary innovation. With operational budgets contracting and expenditures escalating at a rate that surpasses economic growth, healthcare providers increasingly leverage AI not only to drive technological advancement but also to achieve cost optimization. Consequently, the implementation of AI methodologies in real-world healthcare settings has become both logical and necessary. These solutions hold significant potential to streamline workflows, minimize administrative overhead, and enhance service delivery—positioning AI as a critical enabler for addressing clinical and operational challenges under stringent financial constraints.\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eThe incorporation of AI into healthcare operations offers significant potential for enhancing efficiency and accelerating clinical decision-making. As digital transformation advances, AI-driven applications are creating new opportunities to improve both the quality and delivery of healthcare services. Among the most notable benefits is the reduction of turnaround times within clinical workflows, which directly contributes to improved patient outcomes and satisfaction. By automating routine processes—such as data entry, diagnostic evaluations, and follow-up communications—AI allows clinicians to allocate more time to direct patient care, thereby reducing administrative burdens and enhancing overall clinical efficiency. This transition not only optimizes operational performance but also mitigates workload pressures on healthcare professionals.\u003c/p\u003e\u003cp\u003eContemporary AI systems have evolved to perform complex functions traditionally requiring human expertise, including image recognition and interpretation including the by AI agent-based supported navigation using AR and VR platforms, natural language processing, and automatic speech recognition. These capabilities can be integrated into advanced platforms that simultaneously analyze heterogeneous data sources—such as medical imaging, clinical documentation, and verbal interactions—providing real-time insights to support accurate diagnosis and personalized treatment planning. In ocular oncology, such innovations are particularly impactful, facilitating precise evaluation of ocular conditions and streamlining the management of intricate cases.\u003c/p\u003e\u003cp\u003eUltimately, the adoption of AI within healthcare is driving a paradigm shift toward a more responsive, data-centric, and patient-focused model of care. By augmenting, replacing or enhancing manual processes with intelligent systems, the management of healthcare organizations can prove the higher level and significant costs saving in healthcare service delivery.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eConflict of interest\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eOpen access funding provided by The Ministry of Education, Science, Research and Sport of the Slovak Republic in cooperation with Centre for Scientific and Technical Information of the Slovak Republic.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eR.F., P.V., J.V., P.K. and A.F. wrote the main manuscript text. R.F. and M.G. prepared the table. R.F. and M.C. prepared the picture. R.F. and M.C. wrote the results. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eNo datasets were generated or analyzed during the current study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAl Kuwaiti A, Nazer K, Al-Reedy A, Al-Shehri S, Al-Muhanna A, Subbarayalu AV, et al. A Review of the Role of Artificial Intelligence in Healthcare. J Pers Med. 2023;13:951. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/jpm13060951\u003c/span\u003e\u003cspan address=\"10.3390/jpm13060951\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaiyazuddin Md, Rahman SJQ, Anand G, Siddiqui RK, Mehta R, Khatib MN, et al. The Impact of Artificial Intelligence on Healthcare: A Comprehensive Review of Advancements in Diagnostics, Treatment, and Operational Efficiency. 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Mater Today Bio. 2023;23:100792. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.mtbio.2023.100792\u003c/span\u003e\u003cspan address=\"10.1016/j.mtbio.2023.100792\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThurzo A. How is AI Transforming Medical Research, Education and Practice? Bratisl Med J. 2025;126:243\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s44411-025-00063-2\u003c/span\u003e\u003cspan address=\"10.1007/s44411-025-00063-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bratislava-medical-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Bratislava Medical Journal](https://link.springer.com/journal/44411)","snPcode":"44411","submissionUrl":"https://submission.springernature.com/new-submission/44411/3","title":"Bratislava Medical Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Ocular Oncology, Innovation, Management, Artificial Intelligence, Digital transformation","lastPublishedDoi":"10.21203/rs.3.rs-8354984/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8354984/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose:\u003c/h2\u003e \u003cp\u003eA pivotal dimension of healthcare\u0026rsquo;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. Among the most transformative advancements is the deployment of AI-driven methodologies within oncology. Within this context, ocular oncology represents a critical and increasingly prioritized area for innovation. This study concentrates on the possible strategic application of AI technologies from the management perspective with the vision to optimize healthcare processes encompassing, for example, diagnosis, education, therapeutic interventions, and longitudinal monitoring of ocular malignancies.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003ePrior to initiating AI-driven interventions, healthcare management must rigorously assess which use cases yield the greatest potential for strategic and economic impact, given the substantial resource requirements typically associated with such initiatives. 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, critical application requirements are elicited, thereby substantiating the feasibility and relevance of AI-enabled solutions in advancing strategic objectives and contributing to the overarching vision of healthcare digital transformation.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe research process facilitated the identification of several generic use cases within ophthalmology, ultimately converging on ocular oncology as the focal domain. Within this framework, the functional and structural components of ICT were systematically aligned with the established linkages between healthcare management imperatives and the prioritized use cases. The overarching aim of the proposed AI-driven initiative is to advance healthcare management practices through targeted technological integration. For this goal, special applications are envisioned to incorporate the AI-powered modules or AI agents within existing health information systems, in vision supporting also the three-dimensional image data for the augmented and/or virtual reality platforms.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eAI-driven methodologies should be strategically employed to automate non-manufacturing processes within healthcare domains that exhibit the greatest need for innovation, such as ocular oncology. In this context, informed managerial decision-making can substantially accelerate service delivery by reducing turnaround times, mitigating reliance on highly specialized personnel, and ultimately improving patient-centered outcomes and satisfaction in healthcare services.\u003c/p\u003e","manuscriptTitle":"Strategic Perspectives on Artificial Intelligence Applications in Ocular Oncology: Managerial Insights and General Use Cases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-19 17:59:49","doi":"10.21203/rs.3.rs-8354984/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-10T17:58:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-10T14:49:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-24T12:56:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"283563610892653933774966272265917942400","date":"2025-12-24T10:09:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20607985577829721082491209477681804050","date":"2025-12-22T07:09:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-17T04:08:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-15T13:46:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-15T13:46:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Bratislava Medical Journal","date":"2025-12-13T20:56:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bratislava-medical-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Bratislava Medical Journal](https://link.springer.com/journal/44411)","snPcode":"44411","submissionUrl":"https://submission.springernature.com/new-submission/44411/3","title":"Bratislava Medical Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"51c20bdb-3a19-4549-b2d5-e013ba304842","owner":[],"postedDate":"December 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-28T10:54:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-19 17:59:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8354984","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8354984","identity":"rs-8354984","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}