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All Journal Jurnal Sains Manajemen Al-MIKRAJ: Jurnal studi Islam dan Humaniora Integrated System and Management Technology
Rizal Rachman
Universitas Komputer Indonesia
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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Journal : integrated system and management technology

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A Design Science Roadmap for Auditable Ocular Disease Classification: Evidence Mapping of AI Governance Gaps Rizal Rachman; Eddy Soeryanto Soegoto; Irawan Afrianto; Irfan Dwiguna sumitra; Zainal Arifin Hasibuan
Integrated System and Management Technology Vol. 1 No. 2 (2026): April: Integrated System and Management Technology
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/ismat.v1i2.442

Abstract

The integration of Artificial Intelligence (AI) into ocular diagnostics has led to substantial improvements in predictive accuracy. However, a persistent gap remains between technical performance and clinical accountability. The present study addresses the "accuracy trap" and the lack of transparency in current deep learning models for ocular disease classification. The objective of the research is twofold: firstly, to identify methodological deficiencies in extant literature and, secondly, to propose a standardised evaluative framework to ensure model auditability. A systematic evidence mapping (SEM) approach, combined with design science research methodology (DSRM), was utilised to scrutinise 10 high-impact Scopus-indexed studies published between 2023 and 2026. The findings reveal a critical "predictive validity gap," where 80% of the evidence base relies on aggregate accuracy while 90% remains "black box" without functional Explainable AI (XAI) layers. The synthesis of these gaps resulted in the formulation of a conceptual roadmap that mandated multi-metric evaluation, incorporating Cohen's Kappa, and pathophysiological traceability. In conclusion, this research establishes that clinical deployment of AI must transition from model-centric success to a governance-oriented paradigm that prioritises decision utility and auditable audit trails. This roadmap provides a rigorous blueprint for the future implementation of transparent and accountable medical AI systems.
Co-Authors Agus Nursikuwagus Andrias Darmayadi Eddy Soeryanto Soegoto Irawan Afrianto Irfan Dwiguna Sumitra Popon Dauni Sri Erina Damayanti Tri Utomo Wiganarto Usep Mohamad Ishaq Zainal Arifin Hasibuan