Prisma-based systematic review of video based AI applications and challenges in multiple domains
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Abstract
Video-based artificial intelligence has emerged as a rapidly growing field, driven by advancements in deep learning and the increasing demand for automation across sectors. This study aims to summarize the trends, applications, and major challenges in the implementation of video-based AI using a PRISMA-based systematic literature review approach. The data synthesized from 17 selected articles indicates that deep learning models such as CNNs, LSTMs, and hybrid architectures have been successfully employed for various tasks including anomaly detection, deepfake classification, long-range surveillance, video compression, and video-based educational assessment. Applications span across security, healthcare, education, and entertainment, with notable improvements in efficiency and accuracy. However, challenges remain concerning privacy, algorithmic bias, and the gap between technological progress and regulatory readiness. Hardware demands and variability in model performance also pose limitations. These findings underscore the importance of interdisciplinary approaches to foster responsible and sustainable innovation in video-based AI. The review offers a comprehensive overview that may serve as a foundation for future research directions and technological development.
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Li Yaoqi, L. Y. and Rochim, F. P. . (2025) “Prisma-based systematic review of video based AI applications and challenges in multiple domains”, Jurnal Mantik, 9(2), pp. 578-587. doi: 10.35335/mantik.v8i6.6452.
References
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Badidi, E., Moumane, K., & Ghazi, F. El. (2023a). Opportunities, Applications, and Challenges of Edge-AI Enabled Video Analytics in Smart Cities: A Systematic Review. In IEEE Access (Vol. 11, pp. 80543–80572). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2023.3300658
Badidi, E., Moumane, K., & Ghazi, F. El. (2023b). Opportunities, Applications, and Challenges of Edge-AI Enabled Video Analytics in Smart Cities: A Systematic Review. In IEEE Access (Vol. 11, pp. 80543–80572). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2023.3300658
Badidi, E., Moumane, K., & Ghazi, F. E. (2023c). Opportunities, Applications, and Challenges of Edge-AI Enabled Video Analytics in Smart Cities: A Systematic Review. IEEE Access, 11, 80543–80572. https://doi.org/10.1109/ACCESS.2023.3300658
Bin, R., & Nor, M. (n.d.). AI Applications in Education, Healthcare, and Transportation Trends, Challenges, and Future Directions.
Chittam, S., Johnson, D., Roy, K., & Yuan, X. (2024). Deep Fake Video Classification with Sequential Input Frames Using Hybrid Deep Learning Model and Bayesian Optimization. Proceedings - 2024 International Conference on Machine Learning and Applications, ICMLA 2024, 1432–1438. https://doi.org/10.1109/ICMLA61862.2024.00222
Ermishov, N., & Savchenko, A. V. (2024a). Video-Based Emotional Reaction Intensity Estimation Based on Multimodal Feature Extraction. RusAutoCon - Proceedings of the International Russian Automation Conference, 2024, 838–842. https://doi.org/10.1109/RusAutoCon61949.2024.10693960
Ermishov, N., & Savchenko, A. V. (2024b). Video-Based Emotional Reaction Intensity Estimation Based on Multimodal Feature Extraction. RusAutoCon - Proceedings of the International Russian Automation Conference, 2024, 838–842. https://doi.org/10.1109/RusAutoCon61949.2024.10693960
Goyal, G., Gupta, R., Vashishtha, S., Singh, C. K., Aqdas, H., & Garg, H. (2024). Cognitive Video Analysis for Anomaly Detection Using Deep Learning. 2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024. https://doi.org/10.1109/ISCS61804.2024.10581360
Jin, Y. (2024). Visual Analysis of Artificial Intelligence in Short Video Research Based on Citespace. Proceedings - 2024 3rd International Conference on Data Analytics, Computing and Artificial Intelligence, ICDACAI 2024, 126–130. https://doi.org/10.1109/ICDACAI65086.2024.00031
Kantarcioglu, M., & Shaon, F. (2019). Securing big data in the age of AI. Proceedings - 1st IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications, TPS-ISA 2019, 218–220. https://doi.org/10.1109/TPS-ISA48467.2019.00035
Kaur, A., Noori Hoshyar, A., Saikrishna, V., Firmin, S., & Xia, F. (2024). Deepfake video detection: challenges and opportunities. Artificial Intelligence Review, 57(6). https://doi.org/10.1007/s10462-024-10810-6
Khadir, M., Hashmi, M. F., & Khadir, M. (2023). Deep Learning Empowers Next-Gen Video Compression: Bridging Quality and Efficiency. 2023 24th International Arab Conference on Information Technology, ACIT 2023. https://doi.org/10.1109/ACIT58888.2023.10453895
Khanam, M. H., & Roopa, R. (2025). Hybrid Deep Learning Models for Anomaly Detection in CCTV Video Surveillance. 4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025 - Proceedings, 1345–1351. https://doi.org/10.1109/ICSADL65848.2025.10933441
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Lestari, I. S., Jumadi, J., & Lukman, N. (2024). IMPLEMENTASI CONVOLUTIONAL NEURAL NETWORK DENGAN PRE-TRAINED MODEL MOBILENETV2 UNTUK DETEKSI KOLESTEROL. Rabit?: Jurnal Teknologi Dan Sistem Informasi Univrab, 9(2), 173–183. https://doi.org/10.36341/rabit.v9i2.4732
Obaido, G., Mienye, I. D., Egbelowo, O. F., Emmanuel, I. D., Ogunleye, A., Ogbuokiri, B., Mienye, P., & Aruleba, K. (2024). Supervised machine learning in drug discovery and development: Algorithms, applications, challenges, and prospects. Machine Learning with Applications, 17, 100576. https://doi.org/10.1016/j.mlwa.2024.100576
Pattichis, M. S., Jatla, V., & Cerna, A. E. U. (2023). A Review of Machine Learning Methods Applied to Video Analysis Systems. http://arxiv.org/abs/2312.05352
Pinto, J. P., Pimenta, A., & Novais, P. (2021). Deep Learning and Multivariate Time Series for Cheat Detection in Video Games. 2021 IEEE 8th International Conference on Data Science and Advanced Analytics, DSAA 2021. https://doi.org/10.1109/DSAA53316.2021.9564219
Rizwanbasha, A., & Annamalai, R. (2024a). Transforming Crime Scene Investigations Through the Integration of Artificial Intelligence in Digital Forensics. 2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024. https://doi.org/10.1109/IICCCS61609.2024.10763868
Rizwanbasha, A., & Annamalai, R. (2024b). Transforming Crime Scene Investigations Through the Integration of Artificial Intelligence in Digital Forensics. 2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024. https://doi.org/10.1109/IICCCS61609.2024.10763868
Sharma, V., Gupta, M., Kumar, A., & Mishra, D. (2021). Video Processing Using Deep Learning Techniques: A Systematic Literature Review. In IEEE Access (Vol. 9, pp. 139489–139507). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2021.3118541
Singh, D. K., Kumar, A., Suri, D., Kumar, P., Sharma, R., & Sood, N. (2024). Artificial Intelligence in Gaming: A Bibliometric Analysis of Research Outputs and Trends. 2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024. https://doi.org/10.1109/ICIETDW61607.2024.10939117
Singh, D. K., Kumar, P., Sharma, M., & Arora, P. (2023). A Decade of AI and Animation Convergence: A Bibliometric Analysis of Contributions. 2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023. https://doi.org/10.1109/IDICAIEI58380.2023.10406710
Sun, H., Zhu, X., & Zhou, C. (2022). Deep Reinforcement Learning for Video Summarization with Semantic Reward. Proceedings - 2022 IEEE 22nd International Conference on Software Quality, Reliability and Security Companion, QRS-C 2022, 754–755. https://doi.org/10.1109/QRS-C57518.2022.00119
Suresha, M., Kuppa, S., & Raghukumar, D. S. (2020). A study on deep learning spatiotemporal models and feature extraction techniques for video understanding. International Journal of Multimedia Information Retrieval, 9(2), 81–101. https://doi.org/10.1007/s13735-019-00190-x
Umesiobi, V., Alsmadi, H., Hammond, V., Kandasamy, G., & Al Kafri, A. S. (2024a). Utilising Artificial Intelligence to Augment Physiotherapy Education: A Video-Based Analysis and Grading System for Evaluating Student Therapists’ Performance. Proceedings - International Conference on Developments in ESystems Engineering, DeSE, 219–224. https://doi.org/10.1109/DeSE63988.2024.10911886
Umesiobi, V., Alsmadi, H., Hammond, V., Kandasamy, G., & Al Kafri, A. S. (2024b). Utilising Artificial Intelligence to Augment Physiotherapy Education: A Video-Based Analysis and Grading System for Evaluating Student Therapists’ Performance. Proceedings - International Conference on Developments in ESystems Engineering, DeSE, 219–224. https://doi.org/10.1109/DeSE63988.2024.10911886
Vora, D., Kadam, P., Mohite, D. D., Kumar, N., Kumar, N., Radhakrishnan, P., & Bhagwat, S. (2025). AI-driven video summarization for optimizing content retrieval and management through deep learning techniques. Scientific Reports, 15(1), 4058. https://doi.org/10.10438/s41598-025-87824-9
Wei. H, Laszewki. M, & Kehtarnavas. N. (2018). Deep Learning-Based Person Detection and Classification for Far Field Video Surveillance. IEEE.
Zhang, C., Liu, S., Zhou, X., Zhou, S., Tian, Y., Wang, S., Xu, N., & Li, W. (2024). Examining the Role of Large Language Models in Orthopedics: Systematic Review. In Journal of medical Internet research (Vol. 26, p. e59607). https://doi.org/10.2196/59607
Zou, R. (2025). Research on Ethical Issues, Data Privacy Protection, Algorithmic Bias, and Regulatory Policy of Artificial Intelligence Technology in Digital Transformation. In X. Li, C. Yuan, & L. Vartiak (Eds.), Proceedings of the 8th International Conference on Economic Management and Green Development (pp. 234–242). Springer Nature Singapore.
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