Large Language Models (LLMs) in Medical Error Detection and Correction: A Comprehensive Review

The use of Large Language Models (LLMs) in healthcare, specifically for medical error detection and correction, has seen a rise especially in recent years. These errors, if left undetected, can lead to life-threatening consequences; hence, there is a need for advanced solutions. LLMs, with their natural language processing (NLP) ability, can offer valuable opportunities in identifying and correcting medical errors efficiently. This literature review explores various techniques that utilize LLMs for the detection and correction of medical errors. A comprehensive search was conducted across PubMed, IEEE Xplore, and Google Scholar to identify relevant literature. The retrieved research articles were evaluated based on the predefined inclusion and exclusion criteria, and finally, 11 articles were selected. The findings reveal the potential of LLMs in improving healthcare systems by assisting clinicians in their day-to-day work, enhancing clinical decision-making, and automating error detection and correction processes. These models show a promising ability to deliver accurate and timely insights, thereby reducing risks associated with preventable medical errors. Key trends observed include the development and use of fine-tuned medical domain-specific LLMs, integration with electronic health records (EHRs), and applications in real-time clinical settings. Despite their potential, there are still some challenges, such as bias, hallucinations, and ethical concerns. Addressing these issues is crucial to unlock the full potential of LLMs. This review underscores the need for continuous innovation to align LLM capabilities with the dynamic needs of the medical field and procedures.