Potential Applications and Safety of Large Language Models in Healthcare
DOI:
https://doi.org/10.61173/f578jp05Keywords:
large language model, healthcare, privacy security, healthcare big dataAbstract
This study explores the potential applications and associated safety concerns of large language models in healthcare. It particularly highlights the multifaceted applications of large language models (e.g., ChatGPT, MedLM) in healthcare, including data analysis, diagnostics, information retrieval, usage of medical devices, and assistance in tasks. Concurrently, it underscores the risks present alongside these applications, especially concerning data privacy. The study emphasizes the necessity of data privacy protection throughout the entire cycle. By reviewing policies from various countries, it outlines the critical role of refined policies and a clear governmental stance in advancing the application of large language models in healthcare and ensuring their safety.
References
[1] DoctorGPT. (n.d.) DoctorGPT. https://doctorgpt.co.in/
[2] GitHub. (2021) Doctor Dignity. https://github.com/llSourcell/ Doctor-Dignity
[3] Google Cloud. (2023) Use MedLM Models. https://cloud. google.com/vertex-ai/docs/generative-ai/medlm/overview?hl=en
[4] Google Research. (2023) Med-PaLM: A Large Language Model from Google Research, designed for the medical domain. https://sites.research.google/med-palm/?hl=zh-cn
[5] Guo, Z., Luo, Y., Cai, Z., et al. (2021) Overview of Privacy Protection Technology of Big Data in Healthcare. Journal of Frontiers of Computer Science and Technology, 15: 389-402.
[6] Han, P., Gu, L., Zhang, J. (2021) Research on Willingness to Share Medical Data from Perspective of Privacy Protection—— Based on Tripartite Evolutionary Game Analysis. Journal of Modern Information, 41:148-158.
[7] HU, J. (2018) Development Framework and Trend Analysis of Medical Health AI. Chinese Journal of Health Informatics and Management, 15: 485-491.
[8] Li,J., Dada, A., Kleesiek J., et al. (2023) ChatGPT in Healthcare: A Taxonomy and Systematic Review. medRxiv.
[9] Li. X., Ning, S., Akhmetshin. (2023) Legal techniques of the utilization of medical big Data in Russia and enlightenment. China Health Law, 31: 54-59.
[10] McKinsey&Company. (2017) Using Artificial Intelligence to prevent healthcare errors from occurring. In: Second Global Ministerial Summit On Patient Safety.
[11] Xiao, A., Lu, Y., Wu, J., et al. (2021) Privacy Security Mechanism of Biomedical Big Data. Computer Applications and Software, 38: 318-322.
[12] Zhao, Y., Yan, Z., Shen, Q., et al. (2022) Evaluating Privacy Policy for Mobile Health APPs with Machine Learning. Data Analysis and Knowledge Discovery, 6: 112-126.
[13] Kong, X. (2023) Innovation opportunities and challenges of ChatGPT in the medical industry. Zhangjiang Technology Review, 2: 68-71.
[14] Xiong, M., Chi, X. (2023) On the security of generative large language model applications——ChatGPT as example. Shandong Social Sciences, 5: 79-90.
[15] GOV.UK (2023)Approval standard: confidential patient information. https://www.gov.uk/government/publications/ accessing-ukhsa-protected-data/approval-standards-andguidelines-confidential-patient-information
[16] Lamb, S., Tschammler, D., Gottlieb, F., et al. (2023) Health Data in The EU And UK – Regulatory Trends and Developments. https://www.mwe.com/insights/health-data-inthe-eu-and-uk-regulatory-trends-and-developments/
[17] Powles, J., & Hodson, H. (2017). Google DeepMind and healthcare in an age of algorithms. Health and technology, 7(4), 351–367. https://doi.org/10.1007/s12553-017-0179-1
Downloads
Published
Issue
Section
License
Copyright (c) 2024 by the authors.

This work is licensed under a Creative Commons Attribution 4.0 International License.
