Training Large Language Models with Clinical Data: Challenges and Future Directions

Tanmay Shukla

Authors

Tanmay Shukla MS, Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA

Keywords:

Large language models (LLMs), Clinical data, Healthcare AI, Privacy-preserving techniques, Model interpretability, Ethical AI in healthcare, Federated learning, Data standardization and Explainable AI (XAI)

Abstract

LLMs show high performance in multiple fields and applications and could benefit healthcare for better patient management, prediction and decision support. However, the sensitivity and complexity of healthcare data makes it challenging to use clinical data in these models. Here, we examine these issues with reference to the four domains of data privacy, model interpretability, technical limitations and ethical implications; with a view to their relevance in relation to applications in healthcare. We examine the current best practices, suggest approaches for safe data exchange, transparent model interpretation and domain-specific training processes in real clinical settings. Finally, we outline future research directions to help develop LLMs for clinical use that protect patient privacy, which we argue can only be achieved with strong interdisciplinary collaboration and regulation of clinical data use. We hope that our results will help scholars, policymakers, and clinicians navigate toward ethical and efficient solutions for utilizing LLMs in healthcare.

References

Lee, J., et al. (2020). BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4), 1234-1240.

Abd-Alrazaq, A., AlSaad, R., Alhuwail, D., Ahmed, A., Healy, P. M., Latifi, S., ... & Sheikh, J. (2023). Large language models in medical education: opportunities, challenges, and future directions. JMIR Medical Education, 9(1), e48291.

Brown, T. B., et al. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901.

Xu, X., Chen, Y., & Miao, J. (2024). Opportunities, challenges, and future directions of large language models, including ChatGPT in medical education: a systematic scoping review. Journal of Educational Evaluation for Health Professions, 21.

Yadav N, Pandey S, Gupta A, Dudani P, Gupta S, Rangarajan K. Data Privacy in Healthcare: In the Era of Artificial Intelligence. Indian Dermatol Online J. 2023 Oct 27;14(6):788-792. doi: 10.4103/idoj.idoj_543_23. PMID: 38099022; PMCID: PMC10718098.

Wang, F., & Preininger, A. (2019). AI in health: state of the art, challenges, and future directions. Yearbook of medical informatics, 28(01), 016-026.

Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., & Ting, D. S. W. (2023). Large language models in medicine. Nature medicine, 29(8), 1930-1940.

Rudresh Dwivedi, Devam Dave, Het Naik, Smiti Singhal, Rana Omer, Pankesh Patel, Bin Qian, Zhenyu Wen, Tejal Shah, Graham Morgan, and Rajiv Ranjan. 2023. Explainable AI (XAI): Core Ideas, Techniques, and Solutions. ACM Comput. Surv. 55, 9, Article 194 (September 2023), 33 pages. https://doi.org/10.1145/3561048

Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.

Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science, 9(3–4), 211-407.

Bhat P, Shukla T, Naik N, Korir D, R P, Hawa H, Samrot AV, S R, A SS. Deep Neural Network as a Tool to Classify and Identify the 316L and AZ31BMg Metal Surface Morphology: An Empirical Study. Engineered Science. Published online 2023. http://dx.doi.org/10.30919/es1064

Jiang, X., Yan, L., Vavekanand, R., & Hu, M. (2024). Large Language Models in Healthcare Current Development and Future Directions.

Doe, A., & Garcia, K. (2021). Privacy-Preserving Methods for Machine Learning in Healthcare. Medical Data Privacy Journal, 14(1), 37-49.

Lu, Z., Peng, Y., Cohen, T., Ghassemi, M., Weng, C., & Tian, S. (2024). Large language models in biomedicine and health: current research landscape and future directions. Journal of the American Medical Informatics Association, 31(9), 1801-1811.

Ampavathi, A. (2022). Research challenges and future directions towards medical data processing. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 10(6), 633-652.

Chow, J. C., Wong, V., & Li, K. (2024). Generative Pre-Trained Transformer-Empowered Healthcare Conversations: Current Trends, Challenges, and Future Directions in Large Language Model-Enabled Medical Chatbots. BioMedInformatics, 4(1), 837-852.

Subramanian, C. R., Yang, D. A., & Khanna, R. (2024). Enhancing health care communication with large language models—the role, challenges, and future directions. JAMA Network Open, 7(3), e240347-e240347.

Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., ... & Natarajan, V. (2023). Large language models encode clinical knowledge. Nature, 620(7972), 172-180.

Zhou, H., Liu, F., Gu, B., Zou, X., Huang, J., Wu, J., ... & Clifton, D. A. (2023). A survey of large language models in medicine: Progress, application, and challenge. arXiv preprint arXiv:2311.05112.

Khabibullaev, T. (2024). Navigating the Ethical, Organizational, and Societal Impacts of Generative AI: Balancing Innovation with Responsibility. Zenodo. https://doi.org/10.5281/zenodo.13995243

Tan, Z., & Jiang, M. (2023). User modeling in the era of large language models: Current research and future directions. arXiv preprint arXiv:2312.11518.

SHUKLA, TANMAY. "Beyond Diagnosis: AI’s Role in Preventive Healthcare and Early Detection." (2024)

Yao, Y., Zhang, J., Wu, J., Huang, C., Xia, Y., Yu, T., ... & Joe-Wong, C. (2024). Federated Large Language Models: Current Progress and Future Directions. arXiv preprint arXiv:2409.15723.

He, Y., Huang, F., Jiang, X., Nie, Y., Wang, M., Wang, J., & Chen, H. (2024). Foundation model for advancing healthcare: Challenges, opportunities, and future directions. arXiv preprint arXiv:2404.03264.