Unleashing the potential of AI for pathology: challenges and recommendations

被引:17
作者
Asif, Amina [1 ]
Rajpoot, Kashif [2 ]
Graham, Simon [3 ]
Snead, David [3 ,4 ]
Minhas, Fayyaz [1 ,5 ]
Rajpoot, Nasir [1 ,3 ,5 ,6 ]
机构
[1] Univ Warwick, Tissue Image Analyt Ctr, Dept Comp Sci, Coventry, England
[2] Univ Birmingham, Sch Comp Sci, Birmingham, England
[3] Histofy Ltd, Birmingham Business Pk, Birmingham, England
[4] Univ Hosp Coventry & Warwickshire NHS Trust, Dept Pathol, Coventry, England
[5] Univ Warwick, Canc Res Ctr, Coventry, W Midlands, England
[6] Alan Turing Inst, London, England
基金
美国国家卫生研究院;
关键词
artificial intelligence; computational pathology; histopathology; whole slide images; deep learning; machine learning; ARTIFICIAL-INTELLIGENCE; HISTOPATHOLOGY IMAGES; RACIAL BIAS; DEEP; HISTOLOGY; HEALTH; CLASSIFICATION; SEGMENTATION; DISPARITIES; SURVIVAL;
D O I
10.1002/path.6168
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Computational pathology is currently witnessing a surge in the development of AI techniques, offering promise for achieving breakthroughs and significantly impacting the practices of pathology and oncology. These AI methods bring with them the potential to revolutionize diagnostic pipelines as well as treatment planning and overall patient care. Numerous peer-reviewed studies reporting remarkable performance across diverse tasks serve as a testimony to the potential of AI in the field. However, widespread adoption of these methods in clinical and pre-clinical settings still remains a challenge. In this review article, we present a detailed analysis of the major obstacles encountered during the development of effective models and their deployment in practice. We aim to provide readers with an overview of the latest developments, assist them with insights into identifying some specific challenges that may require resolution, and suggest recommendations and potential future research directions. & COPY; 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
引用
收藏
页码:564 / 577
页数:14
相关论文
共 134 条
[1]   Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association [J].
Abels, Esther ;
Pantanowitz, Liron ;
Aeffner, Famke ;
Zarella, Mark D. ;
van der Laak, Jeroen ;
Bui, Marilyn M. ;
Vemuri, Venkata N. P. ;
Parwani, Anil V. ;
Gibbs, Jeff ;
Agosto-Arroyo, Emmanuel ;
Beck, Andrew H. ;
Kozlowski, Cleopatra .
JOURNAL OF PATHOLOGY, 2019, 249 (03) :286-294
[2]   Multimodal biomedical AI [J].
Acosta, Julian N. ;
Falcone, Guido J. ;
Rajpurkar, Pranav ;
Topol, Eric J. .
NATURE MEDICINE, 2022, 28 (09) :1773-1784
[3]   Artificial intelligence as the next step towards precision pathology [J].
Acs, B. ;
Rantalainen, M. ;
Hartman, J. .
JOURNAL OF INTERNAL MEDICINE, 2020, 288 (01) :62-81
[4]   Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey [J].
Akhtar, Naveed ;
Mian, Ajmal .
IEEE ACCESS, 2018, 6 :14410-14430
[5]  
Amgad M., 2021, arXiv
[6]   Weakly supervised learning on unannotated H&E-stained slides predicts BRAF mutation in thyroid cancer with high accuracy [J].
Anand, Deepak ;
Yashashwi, Kumar ;
Kumar, Neeraj ;
Rane, Swapnil ;
Gann, Peter H. ;
Sethi, Amit .
JOURNAL OF PATHOLOGY, 2021, 255 (03) :232-242
[7]  
Anderson C., 2021, CLIN OMICS, V8:, P26
[8]  
Baker M, 2016, NATURE, V533, P452, DOI 10.1038/533452a
[9]   Deep learning in histopathology: A review [J].
Banerji, Sugata ;
Mitra, Sushmita .
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY, 2022, 12 (01)
[10]   Developing image analysis methods for digital pathology [J].
Bankhead, Peter .
JOURNAL OF PATHOLOGY, 2022, 257 (04) :391-402