Medical accuracy of artificial intelligence chatbots in oncology: a scoping review

Recent advances in large language models (LLM) have enabled human-like qualities of natural language competency. Applied to oncology, LLMs have been proposed to serve as an information resource and interpret vast amounts of data as a clinical decision-support tool to improve clinical outcomes.

This review aims to describe the current status of medical accuracy of oncology-related LLM applications and research trends for further areas of investigation.

A scoping literature search was conducted on Ovid Medline for peer-reviewed studies published since 2000. We included primary research studies that evaluated the medical accuracy of a large language model applied in oncology settings. Study characteristics and primary outcomes of included studies were extracted to describe the landscape of oncology-related LLMs.

Sixty studies were included based on the inclusion and exclusion criteria. The majority of studies evaluated LLMs in oncology as a health information resource in question-answer style examinations (48%), followed by diagnosis (20%) and management (17%). The number of studies that evaluated the utility of fine-tuning and prompt-engineering LLMs increased over time from 2022 to 2024. Studies reported the advantages of LLMs as an accurate information resource, reduction of clinician workload, and improved accessibility and readability of clinical information, while noting disadvantages such as poor reliability, hallucinations, and need for clinician oversight.

There exists significant interest in the application of LLMs in clinical oncology, with a particular focus as a medical information resource and clinical decision support tool. However, further research is needed to validate these tools in external hold-out datasets for generalizability and to improve medical accuracy across diverse clinical scenarios, underscoring the need for clinician supervision of these tools.