Generative AI-Powered Clinical Decision Support Using Large Language Models and Electronic Health Records
Keywords:
Generative AI, Clinical Decision Support Systems, Large Language Models, Electronic Health Records, Artificial Intelligence in Healthcare, Clinical NLP, Personalized Medicine, Predictive Clinical Analytics, Retrieval-Augmented Generation, Healthcare InformaticsAbstract
Generative artificial-intelligence (AI) models based on large language models (LLMs) and integration with electronic health records (EHRs) are emerging as a core technology for a new generation of clinical decision-support systems. These systems break away from traditional rule-based reasoning to generate novel responses to user input based on a deep knowledge of medicine typically acquired from training on an extremely large-body of text. Generative AI-powered decision support is particularly attractive in the context of EHR data ecosystems, where a wealth of patient-specific contextual information can be integrated with the general medical knowledge embedded in the LLMs to better support user commands. All language is ultimately generated in response to natural-language user commands and queries, but on the low-level the decision-support function is performed much like a generative text-to-image synthesis model responding to a textual prompt. These points highlight the synergy between LLMs and clinical EHR data.
Although generative AI rose to prominence in the field of image synthesis, the versatile capabilities of LLMs have long been recognized. A great leap forward occurred when OpenAI publicly demonstrated its ChatGPT system in late 2022. Its incredible fluency, human-like conversation with astounding factual capabilities and crude reasoning drew global attention within weeks. Generative AI-based healthcare applications have grown exponentially in number yet lack the feature set and information accuracy required for specific clinical applications. The capability by LLMs to generate text, code, images and video in response to natural-language input is extremely powerful—with its ability to quickly emulate different styles, forms and functions making it more versatile than traditional machine-learning systems.основані на допомозі при прийнятті рішення в медицині.
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[1] AlSaad, R., Abd-alrazaq, A., Boughorbel, S., Ahmed, A., Renault, M.-A., Damseh, R., & Sheikh, J. (2024). Multimodal large language models in health care: Applications, challenges, and future outlook. Journal of Medical Internet Research, 26, e59505.
[2] Ferdush, J., Begum, M., & Hossain, S. T. (2024). ChatGPT and clinical decision support: Scope, application, and limitations. Annals of Biomedical Engineering, 52(5), 1119–1124.
[3] Gilbert, S., Kather, J. N., & Hogan, A. (2024). Augmented non-hallucinating large language models as medical information curators. npj Digital Medicine, 7, 100.
[4] Goh, E., Gallo, R., Hom, J., Strong, E., Weng, Y., Kerman, H., Cool, J. A., Kanjee, Z., Parsons, A. S., Ahuja, N., Horvitz, E., Yang, D., Milstein, A., Olson, A. P. J., Rodman, A., & Chen, J. H. (2024). Large language model influence on diagnostic reasoning: A randomized clinical trial. JAMA Network Open, 7(10), e2440969.
[5] Reddy, V. A. R. (2023). Orchestrating the Future Autonomous Healthcare Data Pipeline Management through Agentic AI Architectures. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(2), 7979-7992.
[6] Guevara, M., Chen, S., Thomas, S., Chaunzwa, T. L., Franco, I., Kann, B. H., Moningi, S., Qian, J. M., Goldstein, M., Harper, S., Aerts, H. J. W. L., Catalano, P. J., Savova, G. K., Mak, R. H., & Bitterman, D. S. (2024). Large language models to identify social determinants of health in electronic health records. npj Digital Medicine, 7, 6.
[7] Kim, J., Leonte, K. G., Chen, M. L., et al. (2024). Large language models outperform mental and medical health care professionals in identifying obsessive-compulsive disorder. npj Digital Medicine, 7, 193.
[8] Klang, E., Apakama, D., Abbott, E. E., Vaid, A., Lampert, J., Sakhuja, A., Freeman, R., Charney, A. W., Reich, D., Kraft, M., Nadkarni, G. N., & Glicksberg, B. S. (2024). A strategy for cost-effective large language model use at health system-scale. npj Digital Medicine, 7, 320.
[9] Kwong, J. C. C., Wang, S. C. Y., Nickel, G. C., et al. (2024). The long but necessary road to responsible use of large language models in healthcare research. npj Digital Medicine, 7, 177.
[10] Liu, S., Wright, A. P., Patterson, B. L., et al. (2024). Using large language model to guide patients to create efficient and comprehensive clinical care message. Journal of the American Medical Informatics Association, 31(8), 1665–1670.
[11] Meng, X., Yan, X., Zhang, K., Liu, D., Cui, X., Yang, Y., Zhang, M., Cao, C., Wang, J., Wang, X., Gao, J., Wang, Y.-G.-S., Ji, J.-M., Qiu, Z., Li, M., Qian, C., Guo, T., Ma, S., Wang, Z., & Tang, Y.-D. (2024). The application of large language models in medicine: A scoping review. iScience, 27(5), 109713.
[12] Moulaei, K., Yadegari, A., Baharestani, M., Farzanbakhsh, S., Sabet, B., & Afrash, M. R. (2024). Generative artificial intelligence in healthcare: A scoping review on benefits, challenges and applications. International Journal of Medical Informatics, 188, 105474.
[13] Nerella, S., Bandyopadhyay, S., Zhang, J., Contreras, M., Siegel, S., Bumin, A., Silva, B., Sena, J., Shickel, B., Bihorac, A., Khezeli, K., & Rashidi, P. (2024). Transformers and large language models in healthcare: A review. Artificial Intelligence in Medicine, 154, 102900.
[14] Ning, Y., Teixayavong, S., Shang, Y., Savulescu, J., Nagaraj, V., Miao, D., Mertens, M., Ting, D. S. W., Ong, J. C. L., Liu, M., Cao, J., Dunn, M., Vaughan, R., Ong, M. E. H., Sung, J. J.-Y., Topol, E. J., & Liu, N. (2024). Generative artificial intelligence and ethical considerations in health care: A scoping review and ethics checklist. The Lancet Digital Health, 6(11), e848–e856.
[15] Omar, M., Nadkarni, G. N., Klang, E., & Glicksberg, B. S. (2024). Large language models in medicine: A review of current clinical trials across healthcare applications. PLOS Digital Health, 3(11), e0000662.
[16] Mangalampalli, B. M. (2023). AI-Driven Anomaly Detection in Healthcare Claims Data: A Business Intelligence Perspective. Journal of Rare Cardiovascular Diseases.
[17] Omiye, J. A., Gui, H., Rezaei, S. J., Zou, J., & Daneshjou, R. (2024). Large language models in medicine: The potentials and pitfalls. Annals of Internal Medicine, 177(2), 210–220.
[18] Ong, C. S., Obey, N. T., Zheng, Y., Cohan, A., & Schneider, E. B. (2024). SurgeryLLM: A retrieval-augmented generation large language model framework for surgical decision support and workflow enhancement. npj Digital Medicine, 7, 364.
[19] Ranji, S. R. (2024). Large language models—Misdiagnosing diagnostic excellence? JAMA Network Open, 7(10), e2440901.
[20] Raza, M. M., Venkatesh, K. P., & Kvedar, J. C. (2024). Generative AI and large language models in health care: Pathways to implementation. npj Digital Medicine, 7, 62.
[21] Riedemann, L., Labonne, M., & Gilbert, S. (2024). The path forward for large language models in medicine is open. npj Digital Medicine, 7, 339.
[22] Rutledge, G. W. (2024). Diagnostic accuracy of GPT-4 on common clinical scenarios and challenging cases. Learning Health Systems, 8(3), e10438.
[23] Sahoo, S. S., Plasek, J. M., Xu, H., et al. (2024). Large language models for biomedicine: Foundations, opportunities, challenges, and best practices. Journal of the American Medical Informatics Association, 31(9), 2114–2124.
[24] Kolla, S. K. (2023). Learning Health Systems Machine Intelligence for Clinical Prediction and Healthcare Optimization. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(2), 7955-7966.
[25] Tam, T. Y. C., Sivarajkumar, S., Kapoor, S., et al. (2024). A framework for human evaluation of large language models in healthcare derived from literature review. npj Digital Medicine, 7, 258.
[26] Tian, S., Jin, Q., Yeganova, L., Lai, P.-T., Zhu, Q., Chen, X., Yang, Z., Chen, Q., Kim, S., Comeau, D. C., Islamaj, R., & Lu, Z. (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.
[27] Mandala, G., Reddy, R., Nishanth, A., Yasmeen, Z., & Maguluri, K. K. (2023). Ai and ml in healthcare: redefining diagnostics, treatment, and personalized medicine. International Journal of Applied Engineering & Technology, 5(S6).
[28] Wang, D., & Zhang, S. (2024). Large language models in medical and healthcare fields: Applications, advances, and challenges. Artificial Intelligence Review, 57, 299.
[29] Wang, L., Wan, Z., Ni, C., Song, Q., Li, Y., Clayton, E. W., Malin, B. A., & Yin, Z. (2024). Applications and concerns of ChatGPT and other conversational large language models in health care: Systematic review. Journal of Medical Internet Research, 26, e22769.
[30] Zhou, W., & Miller, T. A. (2024). Generalizable clinical note section identification with large language models. JAMIA Open, 7(3), ooae075.
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