We regularly try and benchmark new papers, models, libraries, or services that comes out claiming new capabilities in healthcare NLP. This includes recently released models like ChatGPT and BioGPT. Since we get asked about them a lot, this blog posts summaries early finding in benchmarking them versus current state-of-the-art models for medical natural language processing tasks.
Motivation John Snow Labs’ main promise to the healthcare industry is that we will keep you at the state of the art. We’ve reimplemented our core algorithms every year since...
Spark NLP for Healthcare NER models outperform ChatGPT by 10–45% on key medical concepts, resulting in half the errors compared to ChatGPT. Introduction In the last few months, large language...
Spark NLP for Healthcare De-Identification module demonstrates superior performance with a 93% accuracy rate compared to ChatGPT’s 60% accuracy on detecting PHI entities in clinical notes. Organizations handling documents containing...
In assigning ICD10-CM codes, Spark NLP for Healthcare achieved a 76% success rate, while GPT-3.5 and GPT-4 had overall accuracies of 26% and 36% respectively. Introduction In the healthcare industry,...
The potential consequences of “hallucinations” or inaccuracies generated by ChatGPT can be particularly severe in clinical settings. Misinformation generated by LLMs could lead to incorrect diagnoses, improper treatment recommendations, or...
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