Publication date: Oct 23, 2019
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. [Box: see text].
Turner, O.C., Aeffner, F., Bangari, D.S., High, W., Knight, B., Forest, T., Cossic, B., Himmel, L.E., Rudmann, D.G., Bawa, B., Muthuswamy, A., Aina, O.H., Edmondson, E.F., Saravanan, C., Brown, D.L., Sing, T., and Sebastian, M.M. Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article*: Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology. 05641. 2019 Toxicol Pathol.
- Nigel Russell: Creating a Community of Precision Medicine Leaders by Leveraging the Media to Drive Innovation
- Understanding Precision Medicine And AI Within The Life Cycle Of Technology Revolutions
- Microsoft and Novartis team up to transform medicine using AI and data science
- How Real-World Data Could Advance Clinical Trials, Precision Medicine
- Channeling the Power of AI into Personalizing Medicine
- The Coming Age of Molecular Medicine
- How precision medicine is changing laboratory services
- Implementing Artificial Intelligence and Digital Health in Resource-Limited Settings? Top 10 Lessons We Learned in Congenital Heart Defects and Cardiology.
- Artificial Intelligence in the Management of Glioma: Era of Personalized Medicine.
- The use of a machine-learning algorithm that predicts hypotension during surgery in combination with personalized treatment guidance: study protocol for a randomized clinical trial.