TIM-7070 v1

Rodis, N., Sardianos, C., Radoglou-Grammatikis, P., Sarigiannidis, P., Varlamis, I., & Papadopoulos, G. T. (2024). Multimodal explainable artificial intelligence: A comprehensive review of methodological advances and future research directions. IEEE Access, Access, IEEE, 12, 159794–159820. https://doi.org/10.1109/ACCESS.2024.3467062
This article reviews recent developments in Multimodal Explainable AI (MXAI), analyzing methods, datasets, evaluation metrics, and challenges to enhance AI transparency and interpretability across multiple modalities.

Ahmed, M. N., Anand, A., Hussain, M. R., Ahmed, M. M., Khan, I. M., & Rasool, M. A. (2024). Artificial intelligence in fintech: emerging trends and use cases. 2024 IEEE 7th International Conference on Advanced Technologies, Signal and Image Processing (ATSIP), Advanced Technologies, Signal and Image Processing (ATSIP), 2024 IEEE 7th International Conference On, 1, 459–464. https://doi.org/10.1109/ATSIP62566.2024.10638924
This study analyzes the transformative role of Artificial Intelligence in Fintech, covering emerging applications, regulatory challenges, and ethical strategies, while forecasting future trends in AI-driven financial services.

Kolla, L., & Parikh, R. B. (2024). Uses and limitations of artificial intelligence for oncology. Cancer (0008543X), 130(12), 2101–2107. https://doi.org/10.1002/cncr.35307
This article reviews the promise and limitations of predictive and generative AI in oncology, highlighting use cases in diagnosis, prognostication, and patient communication. It emphasizes challenges like bias, explainability, and performance drift while exploring regulatory and design strategies to improve AI safety and reliability in cancer care.

Lateef Junaid, M. A. (2025). Artificial intelligence driven innovations in biochemistry: A review of emerging research frontiers. Biomolecules & Biomedicine, 25(4), 739–750. https://doi.org/10.17305/bb.2024.11537
This review examines how AI is transforming biochemistry by enhancing data analysis, molecular modeling, enzyme engineering, and drug discovery. It highlights tools like AlphaFold and discusses AI’s role in emerging areas such as personalized medicine and synthetic biology.

Yu, D., & Zhao, P. (2024). Research on network clothing design system based on artificial intelligence. Procedia Computer Science, 247, 27–35. https://doi.org/10.1016/j.procs.2024.10.004
This paper explores the integration of artificial intelligence (AI) into online fashion design, highlighting how deep learning, image recognition, and machine learning enable personalized and automated clothing creation.

Zamudio, C., Grigsby, J. L., & Michelsen, M. (2024). RAISE: A new method to develop experimental stimuli for advertising research with image generative artificial intelligence. Journal of Advertising, 1–16. https://doi.org/10.1080/00913367.2024.2438002
This study evaluates the effectiveness of image-generative AI (iGenAI) for producing visual stimuli in advertising research. Introducing the RAISE (Rapid Artificial Intelligence Stimuli for Experiments) method, researchers demonstrate that AI-generated visuals are indistinguishable from those created by humans and meet experimental standards.

Hossain, D., & Chen, J. Y. (2025). A study on neuro-symbolic artificial intelligence: Healthcare perspectives.
https://doi.org/10.48550/arXiv.2503.18213
This article reviews the evolution and convergence of Symbolic and Connectionist AI through the lens of Neuro-Symbolic AI (NeSy), which integrates reasoning and learning capabilities. It analyzes over 977 studies, emphasizing NeSy’s role in advancing healthcare applications such as drug discovery and protein engineering.