Ai-Driven Telemedicine: A Comprehensive Review Of Nlp Models In Healthcare Access
Keywords:
Artificial Intelligence, Bidirectional Encoder Representations from Transformers, Machine Learning, NLP models, Telemedicine
Abstract
Due to demographic, pecuniary and various other factors, healthcare facilities to farthest and rural communities remain as a serious concern in most of the countries. With the advent of telemedicine, robust digital communications and advances computing mechanisms like Artificial Intelligence and Natural Language Processing is integrated. In this research, some of the prominent NLP models like BERT, ClinicalBERT, T5, ClinicalXLNet, and DeBERTa are studied for furtherance of telemedicine facilities. Comparative analysis on the basis of strengths and weaknesses in NLP models is exposed. The work discourses the methodology for typical system architecture of NLP models. We discuss the limitations of existing works and identify the scope for future works. The conclusion highlights the latent of NLP models and various research areas in healthcare access to overcome the challenges. This is the prominent area for future work, the study emphasizes mainly on rural communities.
References
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[20] Kang, D., Wu, H., Yuan, L., et al. (2024). A Beginner’s Guide to Artificial Intelligence for Ophthalmologists. Ophthalmology Therapy, 13, 1841–1855. https://doi.org/10.1007/s40123-024-00958-3
[21] Cascella, M., Scarpati, G., Bignami, E.G., et al. (2023). Utilizing an artificial intelligence framework (conditional generative adversarial network) to enhance telemedicine strategies for cancer pain management. Journal of Anesthesia, Analgesia, and Critical Care, 3, 19. https://doi.org/10.1186/s44158-023-00104-8
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[26] Meinert, E., Milne-Ives, M., Lim, E., Higham, A., Boege, S., de Pennington, N., Bajre, M., Mole, G., Normando, E., Xue, K. (2024). Accuracy and safety of an autonomous artificial intelligence clinical assistant conducting telemedicine follow-up assessment for cataract surgery. eClinicalMedicine, 73, 102692. https://doi.org/10.1016/j.eclinm.2024.102692
[27] Darji, K., Ramoliya, F., Kakkar, R., Gupta, R., Tanwar, S., Garg, D. (2024). DNN-based Secure Remote Patient Data Analysis Framework for Improving Human Life Expectancy in Healthcare 4.0. Procedia Computer Science, 235, 549-558. https://doi.org/10.1016/j.procs.2024.04.054
[28] De Mattei, L., Morato, M.Q., Sidhu, V., Gautam, N., Mendonca, C.T., Tsai, A., Hammer, M., Creighton-Wong, L., Azzam, A. (2024). Are Artificial Intelligence Virtual Simulated Patients (AI-VSP) a Valid Teaching Modality for Health Professional Students? Clinical Simulation in Nursing, 92, 101536. https://doi.org/10.1016/j.ecns.2024.101536
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[34] Mastropaolo, A., Scalabrino, S., Cooper, N., Palacio, D.N., Poshyvanyk, D., Oliveto, R. and Bavota, G., 2021. Studying the usage of text-to-text transfer transformer to support code-related tasks. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE), pp. 336-347. IEEE.
[35] Kale, M. and Rastogi, A., 2020. Text-to-text pre-training for data-to-text tasks. arXiv preprint arXiv:2005.10433.
[36] Zou, B., Ding, Y., Li, J., Yu, B. and Kui, X., 2023. TGRA-P: Task-driven model predicts 90-day mortality from ICU clinical notes on mechanical ventilation. Computer Methods and Programs in Biomedicine, 242, p.107783.
[37] He, P., Liu, X., Gao, J. and Chen, W., 2020. Deberta: Decoding-enhanced bert with disentangled attention. arXiv preprint arXiv:2006.03654.
[38] Ta, H.T., Rahman, A.B.S., Najjar, L. and Gelbukh, A.F., 2022. Transfer Learning from Multilingual DeBERTa for Sexism Identification. In IberLEF@ SEPLN.
[2] Bansal, G., Rajgopal, K., Chamola, V., Xiong, Z., & Niyato, D. (2022). Healthcare in Metaverse: A Survey on Current Metaverse Applications in Healthcare. IEEE Access, 10, 119914-119946. doi: 10.1109/ACCESS.2022.3219845
[3] Ahmad, I., Khan, A., & Ahmad, M. (2022). Emerging Technologies for Next Generation Remote Health Care and Assisted Living. IEEE Access, 10, 56094-56132. doi: 10.1109/ACCESS.2022.3177278
[4] Chamola, V., Hassija, V., Gupta, V., & Guizani, M. (2020). A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing its Impact. IEEE Access, 8, 90225-90265. doi: 10.1109/ACCESS.2020.2992341
[5] Raiaan, M. A. K., Islam, M. R., & Al Mahmud, J. (2024). A Review on Large Language Models: Architectures, Applications, Taxonomies, Open Issues and Challenges. IEEE Access, 12, 26839-26874. doi: 10.1109/ACCESS.2024.3365742
[6] Zhou, B., Yang, G., Shi, Z., & Ma, S. (2024). Natural Language Processing for Smart Healthcare. IEEE Reviews in Biomedical Engineering, 17, 4-18. doi: 10.1109/RBME.2022.3210270
[7] Amosa, T. I., Izhar, L. I. B., Sebastian, P., Ismail, I. B., Ibrahim, O., & Ayinla, S. L. (2023). Clinical Errors From Acronym Use in Electronic Health Record: A Review of NLP-Based Disambiguation Techniques. IEEE Access, 11, 59297-59316. doi: 10.1109/ACCESS.2023.3284682
[8] Njah, Y., Leivadeas, A., Violos, J., & Falkner, M. (2023). Toward Intent-Based Network Automation for Smart Environments: A Healthcare 4.0 Use Case. IEEE Access, 11, 136565-136576. doi: 10.1109/ACCESS.2023.3338189
[9] Olusegun, R., Oladunni, T., Audu, H., Houkpati, Y., & Bengesi, S. (2023). Text Mining and Emotion Classification on Monkeypox Twitter Dataset: A Deep Learning-Natural Language Processing (NLP) Approach. IEEE Access, 11, 49882-49894. doi: 10.1109/ACCESS.2023.3277868
[10] Erberk Uslu, E., Sezer, E., & Anil Guven, Z. (2024). NLP-Powered Healthcare Insights: A Comparative Analysis for Multi-Labelling Classification With MIMIC-CXR Dataset. IEEE Access, 12, 67314-67324. doi: 10.1109/ACCESS.2024.3400007
[11] Jelodar, H., Wang, Y., Orji, R., & Huang, S. (2020). Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach. IEEE Journal of Biomedical and Health Informatics, 24(10), 2733-2742. doi: 10.1109/JBHI.2020.3001216
[12] Habib, M., Faris, M., Alomari, A., & Faris, H. (2021). AltibbiVec: A Word Embedding Model for Medical and Health Applications in the Arabic Language. IEEE Access, 9, 133875-133888. doi: 10.1109/ACCESS.2021.3115617
[13] Magna, A. A. R., Allende-Cid, H., Taramasco, C., Becerra, C., & Figueroa, R. L. (2020). Application of Machine Learning and Word Embeddings in the Classification of Cancer Diagnosis Using Patient Anamnesis. IEEE Access, 8, 106198-106213. doi: 10.1109/ACCESS.2020.3000075
[14] Yu, H., & Zhou, Z. (2021). Optimization of IoT-Based Artificial Intelligence Assisted Telemedicine Health Analysis System. IEEE Access, 9, 85034-85048. doi: 10.1109/ACCESS.2021.3088262
[15] Li, Y., Hwang, K., Shuai, K., Li, Z., & Zomaya, A. (2023). Federated Clouds for Efficient Multitasking in Distributed Artificial Intelligence Applications. IEEE Transactions on Cloud Computing, 11(2), 2084-2095. doi: 10.1109/TCC.2022.3184157
[16] Lewandowski, J., Arochena, H. E., Naguib, R. N. G., Chao, K.-M., & Garcia-Perez, A. (2014). Logic-Centred Architecture for Ubiquitous Health Monitoring. IEEE Journal of Biomedical and Health Informatics, 18(5), 1525-1532. doi: 10.1109/JBHI.2014.2312352
[17] Boonnag, C., Srisawasdi, N., Phonphok, J., & Suwannachot, N. (2023). PACMAN: A Framework for Pulse Oximeter Digit Detection and Reading in a Low-Resource Setting. IEEE Internet of Things Journal, 10(15), 13196-13204. doi: 10.1109/JIOT.2023.3262205
[18] Chen, E., Prakash, S., Janapa Reddi, V., et al. (2023). A framework for integrating artificial intelligence for clinical care with continuous therapeutic monitoring. Nature Biomedical Engineering. https://doi.org/10.1038/s41551-023-01115-0
[19] Grüning, D.J., Kamin, J., Panizza, F., et al. (2024). A framework for promoting online prosocial behaviour via digital interventions. Communications Psychology, 2, 6. https://doi.org/10.1038/s44271-023-00052-7
[20] Kang, D., Wu, H., Yuan, L., et al. (2024). A Beginner’s Guide to Artificial Intelligence for Ophthalmologists. Ophthalmology Therapy, 13, 1841–1855. https://doi.org/10.1007/s40123-024-00958-3
[21] Cascella, M., Scarpati, G., Bignami, E.G., et al. (2023). Utilizing an artificial intelligence framework (conditional generative adversarial network) to enhance telemedicine strategies for cancer pain management. Journal of Anesthesia, Analgesia, and Critical Care, 3, 19. https://doi.org/10.1186/s44158-023-00104-8
[22] Camacho Clavijo, S. (2024). AI assessment tools for decision-making on telemedicine: liability in case of mistakes. Discovery Artificial Intelligence, 4, 24. https://doi.org/10.1007/s44163-024-00117-4
[23] Badawy, W. (2023). Data-driven framework for evaluating digitization and artificial intelligence risk: a comprehensive analysis. AI Ethics. https://doi.org/10.1007/s43681-023-00376-4
[24] Lemke, H.U., Mathis-Ullrich, F. (2024). Design criteria for AI-based IT systems. International Journal of Computer Assisted Radiology and Surgery, 19, 185–190. https://doi.org/10.1007/s11548-024-03064-8
[25] Nagaraja, V.H., Ghosh Dastidar, B., Suri, S., Jani, A.R. (2024). Perspectives and use of telemedicine by doctors in India: A cross-sectional study. Health Policy and Technology, 13(2), 100845. https://doi.org/10.1016/j.hlpt.2024.100845
[26] Meinert, E., Milne-Ives, M., Lim, E., Higham, A., Boege, S., de Pennington, N., Bajre, M., Mole, G., Normando, E., Xue, K. (2024). Accuracy and safety of an autonomous artificial intelligence clinical assistant conducting telemedicine follow-up assessment for cataract surgery. eClinicalMedicine, 73, 102692. https://doi.org/10.1016/j.eclinm.2024.102692
[27] Darji, K., Ramoliya, F., Kakkar, R., Gupta, R., Tanwar, S., Garg, D. (2024). DNN-based Secure Remote Patient Data Analysis Framework for Improving Human Life Expectancy in Healthcare 4.0. Procedia Computer Science, 235, 549-558. https://doi.org/10.1016/j.procs.2024.04.054
[28] De Mattei, L., Morato, M.Q., Sidhu, V., Gautam, N., Mendonca, C.T., Tsai, A., Hammer, M., Creighton-Wong, L., Azzam, A. (2024). Are Artificial Intelligence Virtual Simulated Patients (AI-VSP) a Valid Teaching Modality for Health Professional Students? Clinical Simulation in Nursing, 92, 101536. https://doi.org/10.1016/j.ecns.2024.101536
[29] Hayawi, K., Shahriar, S., Alashwal, H., Serhani, M.A. (2024). Generative AI and large language models: A new frontier in reverse vaccinology. Informatics in Medicine Unlocked, 48, 101533. https://doi.org/10.1016/j.imu.2024.101533
[30] Kapustina, O., Burmakina, P., Gubina, N., Serov, N., Vinogradov, V. (2024). User-friendly and industry-integrated AI for medicinal chemists and pharmaceuticals. Artificial Intelligence Chemistry, 2(2), 100072. https://doi.org/10.1016/j.aichem.2024.100072
[31] Devlin, J., Chang, M.W., Lee, K. and Toutanova, K., 2018. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805.
[32] Huang, K., Altosaar, J. and Ranganath, R., 2019. ClinicalBERT: Modelling clinical notes and predicting hospital readmission. arXiv preprint arXiv:1904.05342.
[33] Alsentzer, E., Murphy, J.R., Boag, W., Weng, W.H., Jin, D., Naumann, T. and McDermott, M., 2019. Publicly available clinical BERT embeddings. arXiv preprint arXiv:1904.03323.
[34] Mastropaolo, A., Scalabrino, S., Cooper, N., Palacio, D.N., Poshyvanyk, D., Oliveto, R. and Bavota, G., 2021. Studying the usage of text-to-text transfer transformer to support code-related tasks. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE), pp. 336-347. IEEE.
[35] Kale, M. and Rastogi, A., 2020. Text-to-text pre-training for data-to-text tasks. arXiv preprint arXiv:2005.10433.
[36] Zou, B., Ding, Y., Li, J., Yu, B. and Kui, X., 2023. TGRA-P: Task-driven model predicts 90-day mortality from ICU clinical notes on mechanical ventilation. Computer Methods and Programs in Biomedicine, 242, p.107783.
[37] He, P., Liu, X., Gao, J. and Chen, W., 2020. Deberta: Decoding-enhanced bert with disentangled attention. arXiv preprint arXiv:2006.03654.
[38] Ta, H.T., Rahman, A.B.S., Najjar, L. and Gelbukh, A.F., 2022. Transfer Learning from Multilingual DeBERTa for Sexism Identification. In IberLEF@ SEPLN.
Published
2024-12-04
How to Cite
Madhuri Zawar, & Pradnya Vikhar. (2024). Ai-Driven Telemedicine: A Comprehensive Review Of Nlp Models In Healthcare Access. Revista Electronica De Veterinaria, 25(2), 554-560. https://doi.org/10.69980/redvet.v25i2.1431
Section
Articles
