The growing reliance of medical professionals for quick and accurate documentation, invoicing, and administrative processing has fueled interest in AI-powered automation tools. Large Language Models (LLMs) such as GPT-4, Med-PaLM 2, BioGPT, and ClinicalBERT have the potential to significantly improve medical documentation, clinical workflows, billing accuracy, and administrative load. This paper examines the revolutionary role of LLMs in clinical documentation and administrative automation, including current advances, real-world implementations, and new solutions. The article emphasizes their ability to generate clinical notes, code medical operations, facilitate patient communication, and navigate intricate insurance systems. It also addresses important issues such as model bias, hallucination risks, data privacy, legal accountability, and workforce preparation. This review seeks to offer an in depth yet comprehensible overview for medical practitioners, informaticians, and policymakers intending to securely incorporate LLMs into their existing structures by assessing important deployments and comparing common LLM platforms. While LLMs hold promise in increasing efficiency in medical facilities, their safe implementation will require human supervision, strong ethical standards, and constant system validation.
Agarwal et al. (Thu,) studied this question.