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...
Large language models (LLMs) have showcased impressive abilities in understanding and generating natural language across various fields, including medical challenge problems. In a recent study by OpenAI, researchers conducted a...
LLMs for information retrieval are exceptional tools for enhancing productivity but inherently lack the ability to differentiate between truth and falsehood and can give "hallucinates” facts. They excel at producing...
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