Welcome to the Github repository for ChatGPT NCCN Evaluation! This project contains the code base, outputs, scoring guidelines, and annotations for evaluating the ChatGPT NCCN model's performance.
This google drive folder contains the annotation/scoring guidelines, disease descriptions, prompts, outputs from the turbo0301/ChatGPT, and the scoring records: https://drive.google.com/drive/folders/1ClSynRGJqAcOhUkYQ9kJmcFAyqMPQWfa?usp=sharing
First, we generated designed four sets of prompt templates to query treatment recommendations. Disease descriptions, consisting of a cancer type with or without an extent of disease, were input into the prompt templates to create the final prompts (see google drive for full list of disease descriptions). Then we ask turbo-0301 outputs for the given prompts using the ChatGPT NCCN model. The model was accessed via the OpenAI API.
After this, we asked four board-certified oncologists to evaluate the outputs based on a set of scoring guidelines. The scoring guidelines were designed to measure the quality of the responses based on factors such as relevance, coherence, and correctness.
Below is a diagram showing the overall process of the ChatGPT NCCN evaluation:
Data were analyzed between March 2 and March 14, 2023. One-third of ChatGPT treatment recommendations were non-concordant with NCCN guidelines, and recommendations varied based on how the question was posed. There was frequent disagreement among annotators in scores, highlighting the ambiguities and challenges of evaluating generative LLM output. Also, all 4 prompt templates yielded the same scores for only 9/26 (34.6%) diagnoses.
There is still much work to be done in terms of improving the model's correctness and robustness. ChatGPT was most likely to provide at least one incorrect recommendation among correct recommendations, an insidious error that is difficult for non-experts to detect.
We hope this research provides insight into areas of concern and needs for future research improvements to the ChatGPT model.
Use of artificial intelligence chatbots for cancer treatment information Shan Chen, Benjamin H Kann, Michael B Foote, Hugo JWL Aerts, Guergana K Savova, Raymond H Mak, Danielle S Bitterman JAMA Oncology. Published online August 24, 2023. doi:10.1001/jamaoncol.2023.2954