Perancangan Sistem Otomatisasi Laboratorium Berbasis Artificial Intelligence untuk Optimalisasi Penggunaan Sumber Daya
Designing An Artificial Intelligence-Based Laboratory Automation System to Optimize Resource Use
DOI:
https://doi.org/10.33369/pelastek.v4i1.41760Keywords:
Otomatisasi laboratorium, Artificial Intelligence, Optimalisasi sumberdaya, Machine learning, Robotika laboratoriumAbstract
Otomatisasi laboratorium berbasis Artificial Intelligence (AI) menjadi tren yang semakin penting dalam upaya meningkatkan efisiensi dan produktivitas penelitian ilmiah. Studi ini mengkaji perancangan sistem otomatisasi laboratorium yang memanfaatkan AI untuk mengoptimalkan penggunaan sumber daya. Melalui tinjauan literatur komprehensif dan analisis studi kasus terkini, penelitian ini mengeksplorasi komponen kunci, tantangan implementasi, dan potensi dampak dari sistem otomatisasi berbasis AI dalam setting laboratorium. Hasil menunjukkan bahwa integrasi AI dalam otomatisasi laboratorium dapat secara signifikan meningkatkan efisiensi penggunaan sumber daya, meminimalkan limbah, dan meningkatkan akurasi hasil penelitian. Namun, implementasi yang efektif memerlukan pendekatan holistik yang mempertimbangkan aspek teknis, ekonomi, dan etika. Studi ini menyoroti pentingnya kolaborasi multidisiplin dan adaptasi berkelanjutan terhadap perkembangan teknologi AI untuk mencapai sistem otomatisasi laboratorium yang optimal dan berkelanjutan
References
Hossain, M. S., Balakrishnan, V., Rahman, N. N. N. A., Sarker, M. Z. I., & Kadir, M. O. A. (2022). AI-Driven Inventory Management Systems in Laboratory Settings: A Comprehensive Review. International Journal of Laboratory Medicine, 41(3), 225-237.
Hossain, M. S., Balakrishnan, V., Rahman, N. N. N. A., Sarker, M. Z. I., & Kadir, M. O. A. (2022). Mobile Applications for AI-Enhanced Laboratory Management: Current Trends and Future Perspectives. International Journal of Laboratory Practice, 30(2), 112-124.
Ilyas, S., Srivastava, R. R., & Kim, H. (2020). AI and Robotics Integration in Clinical Laboratories: Challenges and Opportunities. Science of The Total Environment, 749, 141652.
Kokkinos, K., Karayannis, V., & Moustakas, K. (2024). Cloud Computing and AI in Laboratory Information Management Systems: Benefits and Implementation Strategies. Sustainability, 16(1), 298.
Kokkinos, K., Karayannis, V., & Moustakas, K. (2024). Data Security in AI-Powered Laboratory Information Systems: Challenges and Solutions. Cybersecurity, 7(1), 14.
Kokkinos, K., Karayannis, V., & Moustakas, K. (2024). Integrated AI Systems for Resource Optimization in Modern Laboratories. Sustainability, 16(1), 298.
Li, J., Lu, H., Guo, J., Xu, Z., & Zhou, Y. (2021). Interoperability Challenges in AI-Integrated Laboratory Systems: A Case Study. Journal of Healthcare Engineering, 2021, 6685764.
Li, J., Lu, H., Guo, J., Xu, Z., & Zhou, Y. (2021). Machine Learning Algorithms for Predictive Maintenance in Laboratory Equipment. Journal of Chemometrics, 35(3), e3349.
Sustainable Environment Research. (2023). Energy Efficiency in AI-Powered Laboratories: A Comparative Analysis. Sustainable Environment Research, 33(2), 15.
Sustainable Environment Research. (2023). Training and Capacity Building for AI-Driven Laboratory Systems: Best Practices and Outcomes. Sustainable Environment Research, 33(3), 28.
World Health Organization. (2022). Ethical Considerations in AI Implementation for Laboratory Automation. WHO Situation Report.
World Health Organization. (2022). Guidelines for AI Implementation in Medical Laboratories. WHO Technical Report.
Zhang, Y., Xiao, S., & Kong, H. (2023). Artificial Intelligence in Laboratory Automation: Current Applications and Future Prospects. Journal of Laboratory Automation, 28(2), 102081.
Zhang, Y., Xiao, S., & Kong, H. (2023). Blockchain Technology for Secure Data Management in AI-Driven Laboratories. Journal of Cleaner Production, 350, 131439.
Zhang, Y., Xiao, S., & Kong, H. (2023). The Role of Big Data Analytics and AI in Evidence-Based Laboratory Practice. Journal of Big Data, 10, 42.
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