In this webinar, Veysel will delve into the challenges of and need for text summarization and the importance of summarization in various domains, especially in healthcare. He will cover various...
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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