{"title":"评估 GPT-4O 在识别异常血细胞形态方面的准确性和临床实用性。","authors":"Xinjian Cai, Lili Zhan, Yiteng Lin","doi":"10.1177/20552076241298503","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate the accuracy and clinical utility of GPT-4O in recognizing abnormal blood cell morphology, a critical component of hematologic diagnostics.</p><p><strong>Methods: </strong>GPT-4O's blood cell morphology recognition capabilities were assessed by comparing its performance with hematologists. A total of 70 images from the Chinese National Center for Clinical Laboratories, External Quality Assessment (EQA) from 2022 to 2024 were analyzed. Two experienced hematology experts evaluated GPT-4O's recognition accuracy using a Likert scale.</p><p><strong>Results: </strong>GPT-4O achieved an overall accuracy of 70% in blood cell morphology recognition, significantly lower than the 95.42% accuracy of hematologists (p < 0.05). For peripheral blood smears and bone marrow smears, GPT-4O's accuracy was 77.14% and 62.86% respectively. Likert scale evaluations revealed further discrepancies, with GPT-4O scoring 288.50 out of 350, compared to higher manual scores. GPT-4O accurately recognized certain intracellular inclusions such as Howell-Jolly bodies and Auer rods, while it misidentified fragmented red blood cells as neutrophilic metamyelocytes and oval-shaped red blood cells as sickle cells. Additionally, GPT-4O had difficulty accurately identifying intracellular granules and distinguishing cell nuclei and cytoplasm.</p><p><strong>Conclusion: </strong>GPT-4O's performance in recognizing abnormal blood cell morphology is currently inadequate compared to hematologists. Despite its potential as a supplementary tool, significant improvements in its recognition algorithms and an expanded dataset are necessary for it to be reliable for clinical use. Future research should focus on enhancing GPT-4O's diagnostic accuracy and addressing its current limitations.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11536573/pdf/","citationCount":"0","resultStr":"{\"title\":\"Assessing the accuracy and clinical utility of GPT-4O in abnormal blood cell morphology recognition.\",\"authors\":\"Xinjian Cai, Lili Zhan, Yiteng Lin\",\"doi\":\"10.1177/20552076241298503\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>To evaluate the accuracy and clinical utility of GPT-4O in recognizing abnormal blood cell morphology, a critical component of hematologic diagnostics.</p><p><strong>Methods: </strong>GPT-4O's blood cell morphology recognition capabilities were assessed by comparing its performance with hematologists. A total of 70 images from the Chinese National Center for Clinical Laboratories, External Quality Assessment (EQA) from 2022 to 2024 were analyzed. Two experienced hematology experts evaluated GPT-4O's recognition accuracy using a Likert scale.</p><p><strong>Results: </strong>GPT-4O achieved an overall accuracy of 70% in blood cell morphology recognition, significantly lower than the 95.42% accuracy of hematologists (p < 0.05). For peripheral blood smears and bone marrow smears, GPT-4O's accuracy was 77.14% and 62.86% respectively. Likert scale evaluations revealed further discrepancies, with GPT-4O scoring 288.50 out of 350, compared to higher manual scores. GPT-4O accurately recognized certain intracellular inclusions such as Howell-Jolly bodies and Auer rods, while it misidentified fragmented red blood cells as neutrophilic metamyelocytes and oval-shaped red blood cells as sickle cells. Additionally, GPT-4O had difficulty accurately identifying intracellular granules and distinguishing cell nuclei and cytoplasm.</p><p><strong>Conclusion: </strong>GPT-4O's performance in recognizing abnormal blood cell morphology is currently inadequate compared to hematologists. Despite its potential as a supplementary tool, significant improvements in its recognition algorithms and an expanded dataset are necessary for it to be reliable for clinical use. Future research should focus on enhancing GPT-4O's diagnostic accuracy and addressing its current limitations.</p>\",\"PeriodicalId\":51333,\"journal\":{\"name\":\"DIGITAL HEALTH\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-11-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11536573/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"DIGITAL HEALTH\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/20552076241298503\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"DIGITAL HEALTH","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/20552076241298503","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
Assessing the accuracy and clinical utility of GPT-4O in abnormal blood cell morphology recognition.
Objectives: To evaluate the accuracy and clinical utility of GPT-4O in recognizing abnormal blood cell morphology, a critical component of hematologic diagnostics.
Methods: GPT-4O's blood cell morphology recognition capabilities were assessed by comparing its performance with hematologists. A total of 70 images from the Chinese National Center for Clinical Laboratories, External Quality Assessment (EQA) from 2022 to 2024 were analyzed. Two experienced hematology experts evaluated GPT-4O's recognition accuracy using a Likert scale.
Results: GPT-4O achieved an overall accuracy of 70% in blood cell morphology recognition, significantly lower than the 95.42% accuracy of hematologists (p < 0.05). For peripheral blood smears and bone marrow smears, GPT-4O's accuracy was 77.14% and 62.86% respectively. Likert scale evaluations revealed further discrepancies, with GPT-4O scoring 288.50 out of 350, compared to higher manual scores. GPT-4O accurately recognized certain intracellular inclusions such as Howell-Jolly bodies and Auer rods, while it misidentified fragmented red blood cells as neutrophilic metamyelocytes and oval-shaped red blood cells as sickle cells. Additionally, GPT-4O had difficulty accurately identifying intracellular granules and distinguishing cell nuclei and cytoplasm.
Conclusion: GPT-4O's performance in recognizing abnormal blood cell morphology is currently inadequate compared to hematologists. Despite its potential as a supplementary tool, significant improvements in its recognition algorithms and an expanded dataset are necessary for it to be reliable for clinical use. Future research should focus on enhancing GPT-4O's diagnostic accuracy and addressing its current limitations.