Semi-automated Systematic Review: Main applications and trends of foundation models
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Abstract
This systematic review examines current research topics and applications of foundation models and their relevance to various academic disciplines. To manage and organize a systematic review, we used tools like CADIMA, and for the meta-analysis of selected studies, we relied on the Bibliometrix library in R. Our initial search, following PRISMA methodology, identified 1,161 relevant manuscripts. After carefully analyzing themes and narrowing down our focus, we selected 9 studies that stood out for their relevance to recent applications and developments. This finding points to a strong global interest in this field, with a clear emphasis on applications that benefit people directly. The word cloud shows “Human” as the most prominent term, which tells us that researchers and developers are increasingly focused on using this technology to improve human experiences and address real-world needs. Medical research, in particular,
stands out as a rapidly evolving area where this technology is making strides. This suggests exciting potential for healthcare advancements, ranging from personalized treatments to more accurate diagnostics, with an ultimate goal of enhancing patient care and well-being. Overall,
these results suggest that the field is shifting toward creating meaningful, practical solutions that can make a difference in people’s lives.
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References
Abangan, A. S., Kopp, D., & Faillettaz, R. (2023). Artificial intelligence for fish behavior recognition may unlock fishing gear selectivity. Frontiers in Marine Science, 10. https://doi.org/10.3389/fmars.2023.1010761
S. K. Ahmed, S. Hussein, T. A. Aziz, S. Chakraborty, Md. R. Islam, and K. Dhama, “The power of ChatGPT in revolutionizing rural healthcare delivery,” Health Science Reports, vol. 6, no. 11, Nov. 2023, doi: 10.1002/hsr2.1684.
S. Ahn, “The impending impacts of large language models on medical education,” Korean Journal of Medical Education, vol. 35, no. 1, pp. 103–107, Mar. 2023, doi: 10.3946/kjme.2023.253.
H. Ali, “Generative Pre‐Trained Transformer 4 in healthcare: Challenges, opportunities, and recommendations,” Medicine Advances, vol. 1, no. 2, pp. 163–166, Jun. 2023, doi: 10.1002/med4.21.
H. Ali, J. Qadir, T. Alam, M. Househ, and Z. Shah, “Revolutionizing Healthcare with Foundation AI Models,” in Healthcare Transformation with Informatics and Artificial Intelligence, IOS Press, 2023. doi: 10.3233/shti230533.
S. R. Ali, T. D. Dobbs, H. A. Hutchings, and I. S. Whitaker, “Using ChatGPT to write patient clinic letters,” The Lancet Digital Health, vol. 5, no. 4, pp. e179–e181, Apr. 2023, doi: 10.1016/s2589-7500(23)00048-1.
T. C. Chen et al., “Assessing the clinical reasoning of ChatGPT for mechanical thrombectomy in patients with stroke,” Journal of NeuroInterventional Surgery, vol. 16, no. 3, pp. 253–260, Jan. 2024, doi: 10.1136/jnis-2023-021163.
D. Bietsch, R. Stahlbock, and S. Voß, “Synthetic Data as a Proxy for Real-World Electronic Health Records in the Patient Length of Stay Prediction,” Sustainability, vol. 15, no. 18, p.13690, Sep. 2023, doi: 10.3390/su151813690.
G. Briganti, “How ChatGPT works: a mini review,” European Archives of Oto-Rhino-Laryngology, vol. 281, no. 3, pp. 1565–1569, Nov. 2023, doi: 10.1007/s00405-023-08337-7.
R. C. W. Chau et al.R. C. W. Chau, M. Chong, K. M. Thu, N. S. P. Chu, M. Koohi-Moghadam, R. T.-C. Hsung, C. McGrath, and W. Y. H. Lam, “Artificial intelligence-designed single molar dental prostheses: A protocol of prospective experimental study,” PLOS ONE, vol. 17, no.6, p. e0268535, Jun. 2022, doi: 10.1371/journal.pone.0268535.
B. Chen, Z. Wu, and R. Zhao, “From Fiction to Fact: The Growing Role of Generative AI in Business and Finance,” SSRN Electronic Journal, 2023, doi: 10.2139/ssrn.4528225.
J. Chen, D. Wang, Z. Shao, X. Zhang, M. Ruan, H. Li, and J. Li, “Using Artificial Intelligence to Generate Master-Quality Architectural Designs from Text Descriptions,” Buildings, vol. 13, no. 9, p. 2285, Sep. 2023, doi: 10.3390/buildings13092285.
E. W. Coiera, K. Verspoor, and D. P. Hansen, “We need to chat about artificial intelligence,” Medical Journal of Australia, vol. 219, no. 3, pp. 98–100, Jun. 2023, doi: 10.5694/mja2.51992.
S. Doroudi, “The Intertwined Histories of Artificial Intelligence and Education,” International Journal of Artificial Intelligence in Education, vol. 33, no. 4, pp. 885–928, Oct. 2022, doi: 10.1007/s40593-022-00313-2.
F. Fang and X. Jiang, “The Analysis of Artificial Intelligence Digital Technology in Art Education Under the Internet of Things,” IEEE Access, vol. 12, pp. 22928–22937, 2024, doi: 10.1109/access.2024.3363655.
N. Ferruz and B. Höcker, “Controllable protein design with language models,” Nature Machine Intelligence, vol. 4, no. 6, pp. 521–532, Jun. 2022, doi: 10.1038/s42256-022-00499-z.
G. Paaß and S. Giesselbach, Knowledge Acquired by Foundation Models, Springer International Publishing, 2023, pp. 161–185.
D. Haluza and D. Jungwirth, “Artificial Intelligence and Ten Societal Megatrends: An Exploratory Study Using GPT-3,” Systems, vol. 11, no. 3, p. 120, Feb. 2023, doi: 10.3390/systems11030120.
M. Hao, J. Gong, X. Zeng, C. Liu, Y. Guo, X. Cheng, T. Wang, J. Ma, L. Song, and X. Zhang, “Large Scale Foundation Model on Single-cell Transcriptomics,” May 2023, doi: 10.1101/2023.05.29.542705.
M. Johnson, M. Patel, A. Phipps, M. van der Schaar, D. Boulton, and M. Gibbs, “The potential and pitfalls of artificial intelligence in clinical pharmacology,” CPT: Pharmacometrics & Systems Pharmacology, vol. 12, no. 3, pp. 279–284, Jan. 2023, doi: 10.1002/psp4.12902.
J. Kaddour, J. Harris, M. Mozes, H. Bradley, R. Raileanu, and R. McHardy, “Challenges and Applications of Large Language Models.” arXiv, 2023. doi: 10.48550/ARXIV.2307.10169.
A. Katz, U. Shakir, and B. Chambers, “The Utility of Large Language Models and Generative AI for Education Research.” arXiv, 2023. doi: 10.48550/ARXIV.2305.18125.
T. Kim, J. G. Kim, S. Park, H. S. Kim, N. Kim, H. Ha, S.-K. Choi, C. Tucker, H. Sung, and I. D. Jung, “Virtual surface morphology generation of Ti-6Al-4V directed energy deposition via conditional generative adversarial network,” Virtual and Physical Prototyping, vol. 18, no. 1, Sep. 2022, doi: 10.1080/17452759.2022.2124921.
M. Kayali, A. Lykov, I. Fountalis, N. Vasiloglou, D. Olteanu, and D. Suciu, “CHORUS: Foundation Models for Unified Data Discovery and Exploration,” Jun. 2023, doi: 10.48550/ARXIV.2306.09610.
N. Kleinstreuer and T. Hartung, “Artificial intelligence (AI)—it’s the end of the tox as we know it (and I feel fine)*,” Archives of Toxicology, vol. 98, no. 3, pp. 735–754, Jan. 2024, doi: 10.1007/s00204-023-03666-2.
G. Lan, S. Xiao, J. Yang, J. Wen, and M. Xi, “Generative AI-based Data Completeness Augmentation Algorithm for Data-driven Smart Healthcare,” IEEE Journal of Biomedical and Health Informatics, pp. 1–8, 2024, doi: 10.1109/jbhi.2023.3327485.
J. Li, H. Wang, Z. Deng, M. Pan, and H. Chen, “Restoration of non-structural damaged murals in Shenzhen Bao’an based on a generator–discriminator network,” Heritage Science, vol. 9, no. 1, Jan. 2021, doi: 10.1186/s40494-020-00478-w.
Y. Lu, C. Guo, Y. Dou, X. Dai, and F.-Y. Wang, “Could ChatGPT Imagine: Content Control for Artistic Painting Generation Via Large Language Models,” Journal of Intelligent & Robotic Systems, vol. 109, no. 2, Oct. 2023, doi: 10.1007/s10846-023-01956-6.
C. Liang, “Clustering Analysis of Unlabeled Data and Weak-Label Detection Analysis Method Integrating Soft Computing Technology,” IEEE Access, vol. 12, pp. 6852–6863, 2024, doi: 10.1109/access.2024.3351365.
S. Ma, Z.-S. Xu, and T. Sun, “Parallel Generative Adversarial Imputation Network for Multivariate Missing Time-Series Reconstruction and Its Application to Aeroengines,” IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1–16, 2023, doi: 10.1109/
tim.2023.3307765.
G. Mai, C. Cundy, K. Choi, Y. Hu, N. Lao, and S. Ermon, “Towards a foundation model for geospatial artificial intelligence (vision paper),” in Proceedings of the 30th International Conference on Advances in Geographic Information Systems, in SIGSPATIAL ’22. ACM,
Nov. 2022. doi: 10.1145/3557915.3561043.
L. Marti-Bonmati et al., “Considerations for artificial intelligence clinical impact in oncologic imaging: an AI4HI position paper,” Insights into Imaging, vol. 13, no. 1, May 2022, doi: 10.1186/s13244-022-01220-9.
M. Maskey, R. Ramachandran, T. Lee, and R. Ganti, “Foundation AI Models for Science,” May 2023, doi: 10.5194/egusphere-egu23-2944.
M. C. R. Melo, J. R. M. A. Maasch, and C. de la Fuente-Nunez, “Accelerating antibiotic discovery through artificial intelligence,” Communications Biology, vol. 4, no. 1, Sep. 2021, doi: 10.1038/s42003-021-02586-0.
L. Meng, “The Convolutional Neural Network Text Classification Algorithm in the Information Management of Smart Tourism Based on Internet of Things,” IEEE Access, vol. 12, pp. 3570–3580, 2024, doi: 10.1109/access.2024.3349386.
M. Moor, O. Banerjee, Z. F. H. Abad, H. M. Krumholz, J. Leskovec, E. J. Topol, and P. Rajpurkar, “Foundation models for generalist medical artificial intelligence,” Nature, vol. 616, no. 7956, pp. 259–265, Apr. 2023, doi: 10.1038/s41586-023-05881-4.
A. N. Navaz, M. A. Serhani, H. T. El Kassabi, N. Al-Qirim, and H. Ismail, “Trends, Technologies, and Key Challenges in Smart and Connected Healthcare,” IEEE Access, vol. 9, pp. 74044–74067, 2021, doi: 10.1109/access.2021.3079217.
M. H. Naveed, U. S. Hashmi, N. Tajved, N. Sultan, and A. Imran, “Assessing Deep Generative Models on Time Series Network Data,” IEEE Access, vol. 10, pp. 64601–64617, 2022, doi: 10.1109/access.2022.3177906.
Y. Niu and H. Xue, “Exercise Generation and Student Cognitive Ability Research Based on ChatGPT and Rasch Model,” IEEE Access, vol. 11, pp. 116695–116705, 2023, doi: 10.1109/access.2023.3325741.
O. I. Nwosu, M. G. Crowson, and A. Rameau, “Artificial Intelligence Governance and Otolaryngology‐Head and Neck Surgery,” The Laryngoscope, vol. 133, no. 11, pp. 2868–2870, Sep. 2023, doi: 10.1002/lary.31013.
C. Park, Y. Kim, J.-G. Park, D. Hong, and C. Seo, “Evaluating Differentially Private Generative Adversarial Networks Over Membership Inference Attack,” IEEE Access, vol. 9, pp. 167412–167425, 2021, doi: 10.1109/access.2021.3137278.
S. B. Patel and K. Lam, “ChatGPT: the future of discharge summaries?,” The Lancet Digital Health, vol. 5, no. 3, pp. e107–e108, Mar. 2023, doi: 10.1016/s2589-7500(23)00021-3.
PRISMA, “statement,” PRISMA. 2024.
A. N. Raikov and M. Pirani, “Human-Machine Duality: What’s Next in Cognitive Aspects of Artificial Intelligence?,” IEEE Access, vol. 10, pp. 56296–56315, 2022, doi: 10.1109/access.2022.3177657.
S. Ramesh, J. M. Dolezal, S. Kochanny, E. Lanzel, Y.-L. Chang, C.-T. Wu, C.-I. Jan, T.-C. Chen, C.-H. Hua, C.-J. Kang, C.-Y. Chien, M.-H. Tsai, C.-P. Wang, N. Agrawal, E. Izumchenko, K. Yu, J. Hellstein, M. W. Lingen, A. Chaturvedi, and A. T. Pearson, “Artificial intelligencebased cancer progression prediction of oral premalignant lesions via self-supervised deep learning on histopathology.,” JCO Global Oncology, vol. 9, no. Supplement_1, p. 90, Aug. 2023, doi: 10.1200/go.2023.9.supplement_1.90.
C. J. Sabet, S. S. Bajaj, F. C. Stanford, and L. A. Celi, “Equity in Scientific Publishing: Can Artificial Intelligence Transform the Peer Review Process?,” Mayo Clinic Proceedings: Digital Health, vol. 1, no. 4, pp. 596–600, Dec. 2023, doi: 10.1016/j.mcpdig.2023.10.002.
S. Zhang and D. Metaxas, “On the Challenges and Perspectives of Foundation Models for Medical Image Analysis.” arXiv, 2023. doi: 10.48550/ARXIV.2306.05705.
G. Shmueli, B. Maria Colosimo, D. Martens, R. Padman, M. Saar-Tsechansky, O. R. Liu Sheng, W. N. Street, and K.-L. Tsui, “How Can IJDS Authors, Reviewers, and Editors Use (and Misuse) Generative AI?,” INFORMS Journal on Data Science, vol. 2, no. 1, pp. 1–9, Apr. 2023, doi: 10.1287/ijds.2023.0007.
H. Su, Q. He, and B. Guo, “KPI anomaly detection method for Data Center AIOps based on GRU-GAN,” in 2021 10th International Conference on Internet Computing for Science and Engineering, in ICICSE 2021. ACM, Jul. 2021. doi: 10.1145/3485314.3485323.
Y.-C. Tian, S.-H. Xu, and C. Sylla, “A Novel Three-Staged Generative Model for Skeletonizing Chinese Characters with Versatile Styles,” Journal of Computer Science and Technology, vol. 38, no. 6, pp. 1250–1271, Nov. 2023, doi: 10.1007/s11390-023-1337-8.
A. Tzachor, M. Devare, C. Richards, P. Pypers, A. Ghosh, J. Koo, S. Johal, and B. King, “Large language models and agricultural extension services,” Nature Food, vol. 4, no. 11, pp. 941–948, Nov. 2023, doi: 10.1038/s43016-023-00867-x.
R. Van Noorden, “ChatGPT-like AIs are coming to major science search engines,” Nature, vol. 620, no. 7973, p. 258, Aug. 2023, doi: 10.1038/d41586-023-02470-3.
M. W. Wagner, K. Namdar, A. Biswas, S. Monah, F. Khalvati, and B. B. Ertl-Wagner, “Radiomics, machine learning, and artificial intelligence—what the neuroradiologist needs to know,” Neuroradiology, vol. 63, no. 12, pp. 1957–1967, Sep. 2021, doi: 10.1007/
s00234-021-02813-9.
D. Wang, C. Liu, Z. Zhao, S. Shen, L. Liu, B. Li, H. Hu, M. Wu, L. Lin, X. Zhao, and X. Wang, “GujiBERT and GujiGPT: Construction of Intelligent Information Processing Foundation Language Models for Ancient Texts,” Jul. 2023, doi: 10.48550/ARXIV.2307.05354.
J. Wang, H. Li, L. Wang, and C. Wu, “A multimodal dialogue system for improving user satisfaction via knowledge-enriched response and image recommendation,” Neural Computing and Applications, vol. 35, no. 18, pp. 13187–13206, Mar. 2023, doi: 10.1007/
s00521-023-08409-z.
Y. Wang, Y. Yang, W. Ding, and S. Li, “A Residual-Attention Offline Handwritten Chinese Text Recognition Based on Fully Convolutional Neural Networks,” IEEE Access, vol. 9, pp. 132301–132310, 2021, doi: 10.1109/access.2021.3115606.
R. Watermeyer, L. Phipps, D. Lanclos, and C. Knight, “Generative AI and the Automating of Academia,” Postdigital Science and Education, vol. 6, no. 2, pp. 446–466, Nov. 2023, doi: 10.1007/s42438-023-00440-6.
Q. Xu and P. Li, “Computational Modeling of Language Learning in the Era of Generative Artificial Intelligence: A Response to Open Peer Commentaries,” Language Learning, vol. 73, no. S2, pp. 83–94, Jul. 2023, doi: 10.1111/lang.12605.
K. Yan, X. Chen, X. Zhou, Z. Yan, and J. Ma, “Physical Model Informed Fault Detection and Diagnosis of Air Handling Units Based on Transformer Generative Adversarial Network,”IEEE Transactions on Industrial Informatics, vol. 19, no. 2, pp. 2192–2199, Feb. 2023, doi:
1109/tii.2022.3193733.
S. Yang, O. Nachum, Y. Du, J. Wei, P. Abbeel, and D. Schuurmans, “Foundation Models for Decision Making: Problems, Methods, and Opportunities,” Mar. 2023, doi: 10.48550/ARXIV.2303.04129.
J. J. Yepes-Nuñez, G. Urrútia, M. Romero-García, and S. Alonso-Fernández, “Declaración PRISMA 2020: una guía actualizada para la publicación de revisiones sistemáticas,” Revista Española de Cardiología, vol. 74, no. 9, pp. 790–799, Sep. 2021, doi: https://doi.org/10.1016/j.recesp.2021.06.016.
Y. You, Y. Chen, Y. You, Q. Zhang, and Q. Cao, “Evolutionary Game Analysis of Artificial Intelligence Such as the Generative Pre-Trained Transformer in Future Education,” Sustainability, vol. 15, no. 12, p. 9355, Jun. 2023, doi: 10.3390/su15129355.
B. Zhang, A. Vakanski, and M. Xian, “BI-RADS-NET-V2: A Composite Multi-Task Neural Network for Computer-Aided Diagnosis of Breast Cancer in Ultrasound Images With Semantic and Quantitative Explanations,” IEEE Access, vol. 11, pp. 79480–79494, 2023,
doi: 10.1109/access.2023.3298569.
«Gemini», Google DeepMind, October 30th 2024. Available at: https://deepmind.google/technologies/gemini/. [Accesed: November 8th, 2024]
«A Better Way to Build PCBs | Flux». Accessed: 18 de November 18th, 2024. [Online]. Available at: https://www.flux.ai/p/
O. GMBH, «Eliminador de voz y separador de instrumental IA | LALAL.AI». Accessed: November 10th, 2024. [Online]. Available at: https://www.lalal.ai/es/
«Elimina fondos y crea imágenes de productos», Photoroom. Accessed: November 16th, 2024. [Online]. Available at: https://www.photoroom.com/es