Potential of ChatGPT and GPT-4 for Data Mining of Free-Text CT Reports on Lung Cancer
Heidelberg University · University Hospital Heidelberg · +1 more institution
Abstract
Background The latest large language models (LLMs) solve unseen problems via user-defined text prompts without the need for retraining, offering potentially more efficient information extraction from free-text medical records than manual annotation. Purpose To compare the performance of the LLMs ChatGPT and GPT-4 in data mining and labeling oncologic phenotypes from free-text CT reports on lung cancer by using user-defined prompts. Materials and Methods This retrospective study included patients who underwent lung cancer follow-up CT between September 2021 and March 2023. A subset of 25 reports was reserved for prompt engineering to instruct the LLMs in extracting lesion diameters, labeling metastatic disease,…
Citation impact
- FWCI
- 58.71
- Percentile
- 100%
- References
- 19
Authors
9- MAMatthias A. FinkCorresponding
Heidelberg University, University Hospital Heidelberg, German Center for Lung Research
- ABArved Bischoff
Heidelberg University, University Hospital Heidelberg, German Center for Lung Research
- CAChristoph A. Fink
Heidelberg University, University Hospital Heidelberg, German Center for Lung Research
- MMMartin Moll
Heidelberg University, University Hospital Heidelberg, German Center for Lung Research
- JKJonas Kroschke
Heidelberg University, University Hospital Heidelberg, German Center for Lung Research
Topics & keywords
- Medicine
- McNemar's test
- Lung cancer
- Cancer
- Internal medicine
- Artificial intelligence
- Statistics
- Computer science
- Quality Education