Researcher:
Dur Karasayar, Ayşe Hümeyra

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Master Student

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Ayşe Hümeyra

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Dur Karasayar

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Dur Karasayar, Ayşe Hümeyra

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    Large language models as a rapid and objective tool for pathology report data extraction
    (Federation Turkish Pathology Soc., 2024) Department of Computer Engineering; Department of Computer Engineering; Bolat, Beyza; Eren, Özgür Can; Dur Karasayar, Ayşe Hümeyra; Meriçöz, Çisel Aydın; Demir, Çiğdem Gündüz; Kulaç, İbrahim; Koç Üniversitesi İş Bankası Enfeksiyon Hastalıkları Uygulama ve Araştırma Merkezi (EHAM) / Koç University İşbank Center for Infectious Diseases (KU-IS CID); Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); School of Medicine; Graduate School of Health Sciences; College of Engineering
    Medical institutions continuously create a substantial amount of data that is used for scientific research. One of the departments with a great amount of archived data is the pathology department. Pathology archives hold the potential to create a case series of valuable rare entities or large cohorts of common entities. The major problem in creation of these databases is data extraction which is still commonly done manually and is highly laborious and error prone. For these reasons, we offer using large language models to overcome these challenges. Ten pathology reports of selected resection specimens were retrieved from electronic archives of Ko & ccedil; University Hospital for the initial set. These reports were de-identified and uploaded to ChatGPT and Google Bard. Both algorithms were asked to turn the reports in a synoptic report format that is easy to export to a data editor such as Microsoft Excel or Google Sheets. Both programs created tables with Google Bard facilitating the creation of a spreadsheet from the data automatically. In conclusion, we propose the use of AI-assisted data extraction for academic research purposes, as it may enhance efficiency and precision compared to manual data entry.