TY - JOUR
T1 - Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article*
T2 - Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology
AU - Turner, Oliver C.
AU - Aeffner, Famke
AU - Bangari, Dinesh S.
AU - High, Wanda
AU - Knight, Brian
AU - Forest, Tom
AU - Cossic, Brieuc
AU - Himmel, Lauren E.
AU - Rudmann, Daniel G.
AU - Bawa, Bhupinder
AU - Muthuswamy, Anantharaman
AU - Aina, Olulanu H.
AU - Edmondson, Elijah F.
AU - Saravanan, Chandrassegar
AU - Brown, Danielle L.
AU - Sing, Tobias
AU - Sebastian, Manu M.
N1 - Publisher Copyright:
© The Author(s) 2019.
PY - 2020/2/1
Y1 - 2020/2/1
N2 - Toxicologic pathology is transitioning from analog to digital methods. This transition seems inevitable due to a host of ongoing social and medical technological forces. Of these, artificial intelligence (AI) and in particular machine learning (ML) are globally disruptive, rapidly growing sectors of technology whose impact on the long-established field of histopathology is quickly being realized. The development of increasing numbers of algorithms, peering ever deeper into the histopathological space, has demonstrated to the scientific community that AI pathology platforms are now poised to truly impact the future of precision and personalized medicine. However, as with all great technological advances, there are implementation and adoption challenges. This review aims to define common and relevant AI and ML terminology, describe data generation and interpretation, outline current and potential future business cases, discuss validation and regulatory hurdles, and most importantly, propose how overcoming the challenges of this burgeoning technology may shape toxicologic pathology for years to come, enabling pathologists to contribute even more effectively to answering scientific questions and solving global health issues.*This article is a product of a Special Interest Group of the Society of Toxicologic Pathology (STP). The views expressed in this article are those of the authors and do not necessarily represent the policies, positions, or opinions of the STP.
AB - Toxicologic pathology is transitioning from analog to digital methods. This transition seems inevitable due to a host of ongoing social and medical technological forces. Of these, artificial intelligence (AI) and in particular machine learning (ML) are globally disruptive, rapidly growing sectors of technology whose impact on the long-established field of histopathology is quickly being realized. The development of increasing numbers of algorithms, peering ever deeper into the histopathological space, has demonstrated to the scientific community that AI pathology platforms are now poised to truly impact the future of precision and personalized medicine. However, as with all great technological advances, there are implementation and adoption challenges. This review aims to define common and relevant AI and ML terminology, describe data generation and interpretation, outline current and potential future business cases, discuss validation and regulatory hurdles, and most importantly, propose how overcoming the challenges of this burgeoning technology may shape toxicologic pathology for years to come, enabling pathologists to contribute even more effectively to answering scientific questions and solving global health issues.*This article is a product of a Special Interest Group of the Society of Toxicologic Pathology (STP). The views expressed in this article are those of the authors and do not necessarily represent the policies, positions, or opinions of the STP.
KW - artificial intelligence
KW - deep learning
KW - digital toxicologic pathology
KW - machine learning
KW - neural networks
UR - http://www.scopus.com/inward/record.url?scp=85076490753&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85076490753&partnerID=8YFLogxK
U2 - 10.1177/0192623319881401
DO - 10.1177/0192623319881401
M3 - Article
C2 - 31645203
AN - SCOPUS:85076490753
SN - 0192-6233
VL - 48
SP - 277
EP - 294
JO - Toxicologic pathology
JF - Toxicologic pathology
IS - 2
ER -