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Using 2D CT slices, the study asks Q1 to identify imaging modality and Q2 to classify normal versus pathology. When ChatGPT misclassifies hemorrhages, a guided follow-up (Q3) provides a bleeding clue and asks the hemorrhage type. Results show modality identification was accurate, while Q2 sensitivity was limited, improving substantially under Q3 guidance, especially for hemorrhagic cerebrovascular disease. The work indicates contextual prompting can enhance model reliability and support clinical radiology.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/artificial-intelligence-in-radiology-diagnostic-sensitivity-of-chatgpt-for-detecting-hemorrhages-in-cranial-computed-tomography-scans/434648/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/artificial-intelligence-in-radiology-diagnostic-sensitivity-of-chatgpt-for-detecting-hemorrhages-in-cranial-computed-tomography-scans/434648.png","ImageObject",300,407,{"name":92,"@type":93},"Bintang","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-30","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How was ChatGPT-4V prompted to interpret the CT images?","Question",{"text":112,"@type":113},"The study used a series of questions: Q1 identified the imaging technique, Q2 determined whether the scan was normal or showed pathology, and Q3 added a hemorrhage clue when needed to identify the bleeding type.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What were the diagnostic performance results for Q2 without hemorrhage clues?",{"text":117,"@type":113},"For Q2, sensitivity was 23.6%, specificity was 92.5%, and accuracy was 57.4%. 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Sensitivity rose to 50.9% and accuracy to 71.3%.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},434648,1790702186,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":81,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":29,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144},962085564381,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Copyright @ 2026 Author(s) -[Available online at dirjournal.org.](Available online at dirjournal.org. ORIGINAL)[ ORIGINAL](Available online at dirjournal.org. ORIGINAL) ARTICLE  \nContent of this journal is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.  \nArtificial intelligence in radiology: diagnostic sensitivity of ChatGPT for detecting hemorrhages in cranial computed tomography scans  \n Olga Bayar-Kapıcı 1  Erman Altunışık2  \n Feyza Musabeyoğlu2  \n Şeyda Dev2  Ömer Kaya3  \n1Seyhan State Hospital, Clinic of Radiology, Adana, Türkiye  \n2University of Health SciencesTürkiye, Gaziantep City Hospital, Clinic of Neurology, Gaziantep, Türkiye  \n3Çukurova University Faculty of Medicine, Department of Radiology, Adana, Türkiye  \nCorresponding author: Olga Bayar-Kapıcı  \n[E-mail:](E-mail: olgasahbayar@gmail.com)[ olgasahbayar@gmail.com](E-mail: olgasahbayar@gmail.com)  \nReceived 24 May 2025; revision requested 10 June 2025; accepted 28 June 2025.  \nEpub: 21.07.2025  \nPublication date: 02.01.2026  \nDOI: 10.4274/dir.2025.253456  \nPURPOSE  \nChat Generative Pre-trained Transformer (ChatGPT)-4V, a large language model developed by OpenAI, has been explored for its potential application in radiology. This study assesses ChatGPT- 4V’s diagnostic performance in identifying various types of intracranial hemorrhages in non-contrast cranial computed tomography (CT) images.  \nMETHODS  \nIntracranial hemorrhages were presented to ChatGPT using the clearest 2D imaging slices. The first question,“Q1: Which imaging technique is used in this image?”was asked to determine the imaging modality. ChatGPT was then prompted with the second question,“Q2: What do you see in this image and what is the final diagnosis?”to assess whether the CT scan was normal or showed pathology. For CT scans containing hemorrhage that ChatGPT did not interpret correctly, a follow-up question–“Q3: There is bleeding in this image. Which type of bleeding do you see?”–was used to evaluate whether this guidance influenced its response.  \nRESULTS  \nChatGPT accurately identified the imaging technique (Q1) in all cases but demonstrated difficulty diagnosing epidural hematoma (EDH), subdural hematoma (SDH), and subarachnoid hemorrhage (SAH) when no clues were provided (Q2). When a hemorrhage clue was introduced (Q3), ChatGPT correctly identified EDH in 16.7% of cases, SDH in 60%, and SAH in 15.6%, and achieved 100% diagnostic accuracy for hemorrhagic cerebrovascular disease. Its sensitivity, specificity, and accuracy for Q2 were 23.6%, 92.5%, and 57.4%, respectively. These values improved substantially with the clue in Q3, with sensitivity rising to 50.9% and accuracy to 71.3% . ChatGPT also demonstrated higher diagnostic accuracy in larger hemorrhages in EDH and SDH images.  \nCONCLUSION  \nAlthough the model performs well in recognizing imaging modalities, its diagnostic accuracy substantially improves when guided by additional contextual information.  \nCLINICAL SIGNIFICANCE  \nThese findings suggest that ChatGPT’s diagnostic performance improves with guided prompts, highlighting its potential as a supportive tool in clinical radiology.  \nKEYWORDS  \nArtificial intelligence, intracranial hemorrhages, ChatGPT, computed tomography, hematoma  \nArtificial intelligence (AI) is increasingly being used across various fields to assist humans  \nin quickly accessing information and supporting decision-making processes.1 Oneof the subcategories of AI, large language models (LLMs), is a type of generative AI capable of processing, understanding, and generating human knowledge. LLMs are trained using self-supervised learning, which enables them to predict missing or hidden elements within a text.2 Among these LLMs, Chat Generative Pre-trained Transformer (ChatGPT) is built on the GPT-4 architecture. ChatGPT is a text-based model that supports decision-making  \nYou may cite this article as: Bayar-Kapıcı O, Altunışık E, Musabeyoğlu F, Dev Ş, Kaya Ö. Artificial in","cbCaiqzatZSiFozR","https://ap.wps.com/l/cbCaiqzatZSiFozR","pdf",941997,"English","# PURPOSE\n# METHODS\n# RESULTS\n# CONCLUSION\n# CLINICAL SIGNIFICANCE\n# KEYWORDS","[{\"question\":\"How was ChatGPT-4V prompted to interpret the CT images?\",\"answer\":\"The study used a series of questions: Q1 identified the imaging technique, Q2 determined whether the scan was normal or showed pathology, and Q3 added a hemorrhage clue when needed to identify the bleeding type.\"},{\"question\":\"What were the diagnostic performance results for Q2 without hemorrhage clues?\",\"answer\":\"For Q2, sensitivity was 23.6%, specificity was 92.5%, and accuracy was 57.4%. The model had difficulty diagnosing EDH, SDH, and SAH when no clues were provided.\"},{\"question\":\"How did adding a hemorrhage clue (Q3) affect performance?\",\"answer\":\"With the guidance in Q3, ChatGPT correctly identified EDH in 16.7%, SDH in 60%, and SAH in 15.6%, achieving 100% diagnostic accuracy for hemorrhagic cerebrovascular disease. Sensitivity rose to 50.9% and accuracy to 71.3%.\"}]","Artificial intelligence in radiology - diagnostic sensitivity of ChatGPT for detecting hemorrhages in cranial computed tomography scans | PDF",1790670119,15]