Looking Forward: Cutting-Edge Technologies and Skills for Pathologists in the Future

Author:

Janardhan Kyathanahalli S.1ORCID,Kohnken Rebecca2,Turner Oliver C.3ORCID,Gurumurthy Channabasavaiah B.4,Kovi Ramesh C.5

Affiliation:

1. Integrated Laboratory Systems, Inc, Research Triangle Park, NC, USA

2. Preclinical Safety, AbbVie, North Chicago, IL, USA

3. Preclinical Safety, Novartis, Novartis Institutes for BioMedical Research, East Hanover, NJ, USA

4. Pharmacology and Experimental Neuroscience, University of Nebraska Medical Center, Omaha, NE, USA

5. Experimental Pathology Laboratories, Inc, Research Triangle Park, NC, USA

Abstract

Toxicologic pathology is one of the most valuable fields contributing to the advancement of animal and human health. With the ever-changing technological and economic environment, the basic skill set that pathologists are equipped with may require refinement to address the current and future needs. Periodically, pathologists must add relevant, new skills to their toolbox. The Career Development and Outreach Committee of the Society of Toxicologic Pathology (STP) sponsored a career development workshop entitled “Looking Forward: Cutting-edge Technologies and Skills for Pathologists in the Future” in conjunction with the STP 38th Annual Symposium. Experts were chosen to speak on artificial intelligence, clustered regularly interspaced short palindromic repeats technology, microRNAs, and next-generation sequencing. This article provides a summary of the talks presented at the workshop.

Publisher

SAGE Publications

Subject

Cell Biology,Toxicology,Molecular Biology,Pathology and Forensic Medicine

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Artificial intelligence in career development: a scoping review;Human Resource Development International;2024-04-09

2. Challenges and Opportunities for the Veterinary Pathologist in Biomedical Research;Veterinary Pathology;2020-12-17

3. OBDEX – Open Block Data Exchange System;Artificial Intelligence and Machine Learning for Digital Pathology;2020

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