{"title":"专业还是幻觉?ChatGPT在临床遗传学上的综合评价","authors":"Yingbo Zhang;Shumin Ren;Jiao Wang;Chaoying Zhan;Mengqiao He;Xingyun Liu;Rongrong Wu;Jing Zhao;Cong Wu;Chuanzhu Fan;Bairong Shen","doi":"10.1109/TBDATA.2025.3536939","DOIUrl":null,"url":null,"abstract":"Whether viewed as an expert or as a source of ‘knowledge hallucination’, the use of ChatGPT in medical practice has stirred ongoing debate. This study sought to evaluate ChatGPT's capabilities in the field of clinical genetics, focusing on tasks such as ‘Clinical genetics exams’, ‘Associations between genetic diseases and pathogenic genes’, and ‘Limitations and trends in clinical genetics’. Results indicated that ChatGPT performed exceptionally well in question-answering tasks, particularly in clinical genetics exams and diagnosing single-gene diseases. It also effectively outlined the current limitations and prospective trends in clinical genetics. However, ChatGPT struggled to provide comprehensive answers regarding multi-gene or epigenetic diseases, particularly with respect to genetic variations or chromosomal abnormalities. In terms of systematic summarization and inference, some randomness was evident in ChatGPT's responses. In summary, while ChatGPT possesses a foundational understanding of general knowledge in clinical genetics due to hyperparameter learning, it encounters significant challenges when delving into specialized knowledge and navigating the complexities of clinical genetics, particularly in mitigating ‘Knowledge Hallucination’. To optimize its performance and depth of expertise in clinical genetics, integration with specialized knowledge databases and knowledge graphs is imperative.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 3","pages":"919-932"},"PeriodicalIF":7.5000,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Expertise or Hallucination? A Comprehensive Evaluation of ChatGPT's Aptitude in Clinical Genetics\",\"authors\":\"Yingbo Zhang;Shumin Ren;Jiao Wang;Chaoying Zhan;Mengqiao He;Xingyun Liu;Rongrong Wu;Jing Zhao;Cong Wu;Chuanzhu Fan;Bairong Shen\",\"doi\":\"10.1109/TBDATA.2025.3536939\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Whether viewed as an expert or as a source of ‘knowledge hallucination’, the use of ChatGPT in medical practice has stirred ongoing debate. This study sought to evaluate ChatGPT's capabilities in the field of clinical genetics, focusing on tasks such as ‘Clinical genetics exams’, ‘Associations between genetic diseases and pathogenic genes’, and ‘Limitations and trends in clinical genetics’. Results indicated that ChatGPT performed exceptionally well in question-answering tasks, particularly in clinical genetics exams and diagnosing single-gene diseases. It also effectively outlined the current limitations and prospective trends in clinical genetics. However, ChatGPT struggled to provide comprehensive answers regarding multi-gene or epigenetic diseases, particularly with respect to genetic variations or chromosomal abnormalities. In terms of systematic summarization and inference, some randomness was evident in ChatGPT's responses. In summary, while ChatGPT possesses a foundational understanding of general knowledge in clinical genetics due to hyperparameter learning, it encounters significant challenges when delving into specialized knowledge and navigating the complexities of clinical genetics, particularly in mitigating ‘Knowledge Hallucination’. To optimize its performance and depth of expertise in clinical genetics, integration with specialized knowledge databases and knowledge graphs is imperative.\",\"PeriodicalId\":13106,\"journal\":{\"name\":\"IEEE Transactions on Big Data\",\"volume\":\"11 3\",\"pages\":\"919-932\"},\"PeriodicalIF\":7.5000,\"publicationDate\":\"2025-01-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Big Data\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10858419/\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Big Data","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10858419/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Expertise or Hallucination? A Comprehensive Evaluation of ChatGPT's Aptitude in Clinical Genetics
Whether viewed as an expert or as a source of ‘knowledge hallucination’, the use of ChatGPT in medical practice has stirred ongoing debate. This study sought to evaluate ChatGPT's capabilities in the field of clinical genetics, focusing on tasks such as ‘Clinical genetics exams’, ‘Associations between genetic diseases and pathogenic genes’, and ‘Limitations and trends in clinical genetics’. Results indicated that ChatGPT performed exceptionally well in question-answering tasks, particularly in clinical genetics exams and diagnosing single-gene diseases. It also effectively outlined the current limitations and prospective trends in clinical genetics. However, ChatGPT struggled to provide comprehensive answers regarding multi-gene or epigenetic diseases, particularly with respect to genetic variations or chromosomal abnormalities. In terms of systematic summarization and inference, some randomness was evident in ChatGPT's responses. In summary, while ChatGPT possesses a foundational understanding of general knowledge in clinical genetics due to hyperparameter learning, it encounters significant challenges when delving into specialized knowledge and navigating the complexities of clinical genetics, particularly in mitigating ‘Knowledge Hallucination’. To optimize its performance and depth of expertise in clinical genetics, integration with specialized knowledge databases and knowledge graphs is imperative.
期刊介绍:
The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.