The Impact of Large Language Models on Healthcare

Abstract

Healthcare will significantly shift, and LLMs will play a significant role in this industry. Retraining the use of medical databases or generic LLMs to finetune might help develop strategies to improve the usability of LLMs for healthcare. Additionally, multimodal LLMs feature fusion using various data sources, including tabular and visual data, which leads to even greater performance than humans. Even though there are many advantages to using LLMs, it is essential to acknowledge that the content created cannot be solely the responsibility of LLMs. To prevent any potential harm, it is crucial to thoroughly evaluate anything AI generates. Reducing the bar for LLM deployments means that there is more time to focus on improving deployment requirements. To better assist clinical decision-making, efforts should encourage incorporating LLMs in medicine, strengthen human-machine collaboration, and enhance LLM interpretability in clinical contexts. Physicians may optimize the advantages of AI while minimizing possible hazards and improving patient outcomes by utilizing LLMs as an auxiliary tool. Ultimately, the cooperation of administrators, patients, data scientists, doctors, and regulatory agencies will be necessary to integrate LLMs into healthcare effectively.

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