Clinical Acronym Disambiguation via ChatGPT and BING

Clinical texts are written with acronyms, abbreviations and medical jargon expressions to save time. This hinders full comprehension not just for medical experts but also laypeople. This paper attempts to disambiguate acronyms with their given context by comparing a web mining approach via the search engine BING and a conversational agent approach using ChatGPT with the aim to see, if these methods can supply a viable resolution for the input acronym. Both approaches are automated via application programming interfaces. Possible term candidates are extracted using natural language processing-oriented functionality. The conversational agent approach surpasses the baseline for web mining without plausibility thresholds in precision, recall and F1-measure, while scoring similarly only in precision for high threshold values.