Pengembangan Model Kepercayaan Dan Penerimaan Artificial Intelligence dalam Pengambilan Keputusan Klinis Menggunakan Integrasi Tam dan Explainable AI Framework

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Published: Jul 12, 2026
DOI: https://doi.org/10.62357/jsit.v5i2.1181
Keywords:
Artificial Intelligence Technology Acceptance Model Explainable Artificial Intelligence Clinical Decision-Making Healthcare Technology Adoption

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Firdaus daus
Universitas Putra Indonesia YPTK Padang

Abstract

Penerapan Kecerdasan Buatan (AI) dalam pengambilan keputusan klinis telah meningkat secara signifikan karena kemampuannya untuk mendukung diagnosis, perencanaan pengobatan, dan manajemen perawatan kesehatan. Namun, efektivitas implementasi AI sebagian besar bergantung pada kepercayaan dan penerimaan rekomendasi yang dihasilkan AI oleh para profesional kesehatan. Studi ini bertujuan untuk mengembangkan model komprehensif tentang penerimaan dan kepercayaan AI dalam pengambilan keputusan klinis dengan mengintegrasikan Model Penerimaan Teknologi (TAM) dengan kerangka kerja Kecerdasan Buatan yang Dapat Dijelaskan (XAI). Model yang diusulkan menyelidiki pengaruh kegunaan yang dirasakan, kemudahan penggunaan yang dirasakan, kemampuan menjelaskan, kepercayaan pada AI, niat perilaku, dan kualitas keputusan klinis. Desain penelitian kuantitatif akan digunakan dengan mengumpulkan data dari dokter, perawat, dan praktisi kesehatan yang berinteraksi dengan sistem perawatan kesehatan berbasis AI. Pemodelan Persamaan Struktural (SEM) akan digunakan untuk mengevaluasi hubungan yang diusulkan dan memvalidasi kerangka kerja konseptual. Temuan yang diharapkan akan memberikan pemahaman yang lebih dalam tentang faktor-faktor yang memengaruhi kesediaan para profesional kesehatan untuk mengadopsi teknologi AI dan peran kemampuan menjelaskan dalam membangun kepercayaan terhadap sistem AI. Studi ini berkontribusi pada kemajuan teori adopsi AI di bidang perawatan kesehatan dan memberikan implikasi praktis untuk mengembangkan solusi AI yang transparan, andal, dan berpusat pada pengguna di lingkungan klinis.

Article Details

Issue
Vol. 5 No. 2 (2026): Jurnal Sains Informatika Terapan (Juni, 2026)
Section
Articles
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References

Adadi, A., & Berrada, M. (2018). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115.

Albahri, A. S., Duhaim, A. M., Fadhel, M. A., et al. (2023). A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion. Information Fusion, 96, 156–191.

Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020). Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Medical Informatics and Decision Making, 20(310), 1–9.

Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., et al. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115.

Choudhury, A., & Asan, O. (2022). Role of AI transparency and trust in clinical decision support adoption. Journal of Medical Systems, 46(5), 1–12.

Davenport, T. H., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

Dave, D., Naik, H., Singhal, S., & Patel, P. (2020). Explainable AI meets healthcare: A study on heart disease dataset. arXiv Preprint.

Ghassemi, M., Oakden-Rayner, L., & Beam, A. L. (2021). The false hope of current approaches to explainable artificial intelligence in health care. The Lancet Digital Health, 3(11), e745–e750.

Guidotti, R., Monreale, A., Ruggieri, S., et al. (2018). A survey of methods for explaining black box models. ACM Computing Surveys, 51(5), 1–42.

Haleem, A., Javaid, M., Singh, R. P., & Suman, R. (2023). Artificial intelligence applications for healthcare: Opportunities and challenges. Healthcare Analytics, 3, 100090.

Jones, C., Thornton, J., & Wyatt, J. C. (2021). Enhancing trust in clinical decision support systems: A framework for developers. BMJ Health & Care Informatics, 28(1), e100247.

Kiseleva, A., Kotzinos, D., & De Hert, P. (2023). Transparency and explainability requirements for artificial intelligence in healthcare. AI & Society, 38(2), 541–553.

Loh, H. W., Ooi, C. P., Seoni, S., Barua, P. D., Molinari, F., & Acharya, U. R. (2022). Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011–2022). Computer Methods and Programs in Biomedicine, 226, 107161.

Nasarian, E., Alizadehsani, R., Acharya, U. R., & Tsui, K. L. (2024). Designing interpretable ML system to enhance trust in healthcare: A systematic review to proposed responsible clinician-AI-collaboration framework. Information Fusion, 108, 102412.

Rosenbacke, R., Melhus, Å., McKee, M., & Stuckler, D. (2024). How Explainable Artificial Intelligence can increase or decrease clinicians' trust in AI applications in healthcare: Systematic review. JMIR AI, 3, e53207.

Sadeghi, Z., Alizadehsani, R., Cifci, M. A., et al. (2023). A brief review of explainable artificial intelligence in healthcare. arXiv Preprint.

Saraswat, D., Bhattacharya, P., Verma, A., et al. (2022). Explainable AI for Healthcare 5.0: Opportunities and challenges. IEEE Access, 10, 84486–84517.

Siau, K., & Wang, W. (2018). Building trust in artificial intelligence, machine learning, and robotics. Cutting Edge Business Technologies in the Big Data Era, 47–53.

Topol, E. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.

Taneja, A. (2023). Explainable AI in healthcare: Ensuring trust and transparency in machine learning clinical decision systems. International Journal of Artificial Intelligence, Data Science and Machine Learning, 4(1), 51–59.

van Dort, B. A., López Cañizares, J., Marcilly, R., Peute, L., & Sent, D. (2025). Enhancing clinical decision support: A heuristic evaluation of explainable AI in healthcare dashboards. Studies in Health Technology and Informatics, 327, 343–347.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

Wysocki, O., Davies, J. K., Vigo, M., Armstrong, A. C., Landers, D., Lee, R., & Freitas, A. (2023). Assessing the communication gap between AI models and healthcare professionals: Explainability, utility and trust in AI-driven clinical decision-making. Artificial Intelligence, 316, 103839.

Yang, C. C. (2022). Explainable artificial intelligence for predictive modelling in healthcare. Journal of Healthcare Informatics Research, 6(2), 228–239.

Zhang, Y., Chen, M., & Li, X. (2024). Artificial intelligence literacy and healthcare professionals’ readiness for AI adoption in hospitals. Healthcare Analytics, 4, 100245.