Sharon Ruane, Korsuk Sirinukunwattana, Anna Kotanska, Alan Aberdeen
{"title":"为组织病理学开发人工智能工具:机遇、挑战和新朋友","authors":"Sharon Ruane, Korsuk Sirinukunwattana, Anna Kotanska, Alan Aberdeen","doi":"10.1016/j.mpdhp.2025.03.003","DOIUrl":null,"url":null,"abstract":"<div><div>Advancements in slide digitization and artificial intelligence (AI) offer immense transformative potential for pathology. While much focus is placed on AI's potential to automate tasks and standardize assessments, its most significant impact may come from uncovering novel tissue-based biomarkers and deepening our understanding of disease. Properly developed and validated AI-based tools could improve the quantification of known biomarkers, identify novel tissue-based biomarkers beyond human perception, and enable inter-sample comparisons to reveal disease subtypes and heterogeneity. This article draws on our practical experience working with pathologists to develop AI-based algorithms for assessing bone marrow in patients with blood cancer. We provide an overview of approaches to AI model development from perspectives typically of most interest to our pathologist collaborators: the data requirements and the resulting model interpretability. We discuss the limitations of the current manual assessment of histopathology samples and the opportunities provided by AI-based approaches. We then address major challenges in the field and discuss how an interdisciplinary approach combining expertise across disciplines is essential to maximizing the potential of AI-powered pathology tools.</div></div>","PeriodicalId":39961,"journal":{"name":"Diagnostic Histopathology","volume":"31 5","pages":"Pages 277-283"},"PeriodicalIF":0.0000,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Developing AI-powered tools for histopathology: opportunities, challenges and new friends along the way\",\"authors\":\"Sharon Ruane, Korsuk Sirinukunwattana, Anna Kotanska, Alan Aberdeen\",\"doi\":\"10.1016/j.mpdhp.2025.03.003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Advancements in slide digitization and artificial intelligence (AI) offer immense transformative potential for pathology. While much focus is placed on AI's potential to automate tasks and standardize assessments, its most significant impact may come from uncovering novel tissue-based biomarkers and deepening our understanding of disease. Properly developed and validated AI-based tools could improve the quantification of known biomarkers, identify novel tissue-based biomarkers beyond human perception, and enable inter-sample comparisons to reveal disease subtypes and heterogeneity. This article draws on our practical experience working with pathologists to develop AI-based algorithms for assessing bone marrow in patients with blood cancer. We provide an overview of approaches to AI model development from perspectives typically of most interest to our pathologist collaborators: the data requirements and the resulting model interpretability. We discuss the limitations of the current manual assessment of histopathology samples and the opportunities provided by AI-based approaches. We then address major challenges in the field and discuss how an interdisciplinary approach combining expertise across disciplines is essential to maximizing the potential of AI-powered pathology tools.</div></div>\",\"PeriodicalId\":39961,\"journal\":{\"name\":\"Diagnostic Histopathology\",\"volume\":\"31 5\",\"pages\":\"Pages 277-283\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Diagnostic Histopathology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1756231725000349\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Diagnostic Histopathology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1756231725000349","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Developing AI-powered tools for histopathology: opportunities, challenges and new friends along the way
Advancements in slide digitization and artificial intelligence (AI) offer immense transformative potential for pathology. While much focus is placed on AI's potential to automate tasks and standardize assessments, its most significant impact may come from uncovering novel tissue-based biomarkers and deepening our understanding of disease. Properly developed and validated AI-based tools could improve the quantification of known biomarkers, identify novel tissue-based biomarkers beyond human perception, and enable inter-sample comparisons to reveal disease subtypes and heterogeneity. This article draws on our practical experience working with pathologists to develop AI-based algorithms for assessing bone marrow in patients with blood cancer. We provide an overview of approaches to AI model development from perspectives typically of most interest to our pathologist collaborators: the data requirements and the resulting model interpretability. We discuss the limitations of the current manual assessment of histopathology samples and the opportunities provided by AI-based approaches. We then address major challenges in the field and discuss how an interdisciplinary approach combining expertise across disciplines is essential to maximizing the potential of AI-powered pathology tools.
期刊介绍:
This monthly review journal aims to provide the practising diagnostic pathologist and trainee pathologist with up-to-date reviews on histopathology and cytology and related technical advances. Each issue contains invited articles on a variety of topics from experts in the field and includes a mini-symposium exploring one subject in greater depth. Articles consist of system-based, disease-based reviews and advances in technology. They update the readers on day-to-day diagnostic work and keep them informed of important new developments. An additional feature is the short section devoted to hypotheses; these have been refereed. There is also a correspondence section.