Norbert Gal-Nadasan, Vasile Stoicu-Tivadar, Emanuela Gal-Nadasan, Anca Raluca Dinu
{"title":"Handwritten Data Extraction Using OpenAI ChatGPT4o and Robotic Process Automation.","authors":"Norbert Gal-Nadasan, Vasile Stoicu-Tivadar, Emanuela Gal-Nadasan, Anca Raluca Dinu","doi":"10.3233/SHTI241101","DOIUrl":null,"url":null,"abstract":"<p><p>This paper proposes to create an Robotic Process Automation style application that can digitalize and extract data from handwritten medical forms. The RPA robot uses OpenAI ChatGPT4o model to extract handwritten medical data and transform it into typed data. The handwritten data is transcribed correctly at a rate of 100%. The data interpretation is accomplished by the UiPath machine learning API. By creating new nonstandard form templates and associated taxonomies the system can be scaled as desired. After the data extraction process the saved data can be sent to a database, spreadsheet. The access to this medical data is restricted to the physicians and medical nurses employed at the medical facility.</p>","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"321 ","pages":"245-249"},"PeriodicalIF":0.0000,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Studies in health technology and informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/SHTI241101","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper proposes to create an Robotic Process Automation style application that can digitalize and extract data from handwritten medical forms. The RPA robot uses OpenAI ChatGPT4o model to extract handwritten medical data and transform it into typed data. The handwritten data is transcribed correctly at a rate of 100%. The data interpretation is accomplished by the UiPath machine learning API. By creating new nonstandard form templates and associated taxonomies the system can be scaled as desired. After the data extraction process the saved data can be sent to a database, spreadsheet. The access to this medical data is restricted to the physicians and medical nurses employed at the medical facility.
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