Shelly Soffer, Vera Sorin, Girish N. Nadkarni, Eyal Klang
{"title":"大型语言模型在医学伦理推理中的缺陷","authors":"Shelly Soffer, Vera Sorin, Girish N. Nadkarni, Eyal Klang","doi":"10.1038/s41746-025-01792-y","DOIUrl":null,"url":null,"abstract":"Large language models (LLMs), such as ChatGPT-o1, display subtle blind spots in complex reasoning tasks. We illustrate these pitfalls with lateral thinking puzzles and medical ethics scenarios. Our observations indicate that patterns in training data may contribute to cognitive biases, limiting the models’ ability to navigate nuanced ethical situations. Recognizing these tendencies is crucial for responsible AI deployment in clinical contexts.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"06 1","pages":""},"PeriodicalIF":15.1000,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Pitfalls of large language models in medical ethics reasoning\",\"authors\":\"Shelly Soffer, Vera Sorin, Girish N. Nadkarni, Eyal Klang\",\"doi\":\"10.1038/s41746-025-01792-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Large language models (LLMs), such as ChatGPT-o1, display subtle blind spots in complex reasoning tasks. We illustrate these pitfalls with lateral thinking puzzles and medical ethics scenarios. Our observations indicate that patterns in training data may contribute to cognitive biases, limiting the models’ ability to navigate nuanced ethical situations. Recognizing these tendencies is crucial for responsible AI deployment in clinical contexts.\",\"PeriodicalId\":19349,\"journal\":{\"name\":\"NPJ Digital Medicine\",\"volume\":\"06 1\",\"pages\":\"\"},\"PeriodicalIF\":15.1000,\"publicationDate\":\"2025-07-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"NPJ Digital Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1038/s41746-025-01792-y\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"NPJ Digital Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1038/s41746-025-01792-y","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
Pitfalls of large language models in medical ethics reasoning
Large language models (LLMs), such as ChatGPT-o1, display subtle blind spots in complex reasoning tasks. We illustrate these pitfalls with lateral thinking puzzles and medical ethics scenarios. Our observations indicate that patterns in training data may contribute to cognitive biases, limiting the models’ ability to navigate nuanced ethical situations. Recognizing these tendencies is crucial for responsible AI deployment in clinical contexts.
期刊介绍:
npj Digital Medicine is an online open-access journal that focuses on publishing peer-reviewed research in the field of digital medicine. The journal covers various aspects of digital medicine, including the application and implementation of digital and mobile technologies in clinical settings, virtual healthcare, and the use of artificial intelligence and informatics.
The primary goal of the journal is to support innovation and the advancement of healthcare through the integration of new digital and mobile technologies. When determining if a manuscript is suitable for publication, the journal considers four important criteria: novelty, clinical relevance, scientific rigor, and digital innovation.