2024/06/24 by Joshua Pantanowitz, Christopher D. Manko, Christopher D Manko +2 · 1 citation
Computer Science · Health Professions · #AI in cancer detection #Artificial Intelligence in Healthcare #Machine Learning in Healthcare
paper · doi:10.1016/j.labinv.2024.102095
openalex publication_date 2024/06/24 · crossref created 2024/06/24 · crossref issued 2024/08/01 · crossref published 2024/08/01 · crossref published-print 2024/08/01 · crossref deposited 2025/01/08 · openalex created_date 2025/10/10 · crossref indexed 2026/07/31 · openalex updated_date 2026/08/01
In our rapidly expanding landscape of artificial intelligence, synthetic data have become a topic of great promise and also some concern. This review aimed to provide pathologists and laboratory professionals with a primer on the role of synthetic data and how it may soon shape the landscape within our field. Using synthetic data presents many advantages but also introduces a milieu of new obstacles and limitations. This review aimed to provide pathologists and laboratory professionals with a primer on the general concept of synthetic data and its potential to transform our field. By leveraging synthetic data, we can help accelerate the development of various machine learning models and enhance our medical education and research/quality study needs. This review explored the methods for generating synthetic data, including rule-based, machine learning model-based and hybrid approaches, as they apply to applications within pathology and laboratory medicine. We also discussed the limitations and challenges associated with such synthetic data, including data quality, malicious use, and ethical bias/concerns and challenges. By understanding the potential benefits (ie, medical education, training artificial intelligence programs, and proficiency testing, etc) and limitations of this new data realm, we can not only harness its power to improve patient outcomes, advance research, and enhance the practice of pathology but also become readily aware of their intrinsic limitations.