| Literature DB >> 30962048 |
Jacob L Jaremko1, Marleine Azar2, Rebecca Bromwich3, Andrea Lum4, Li Hsia Alicia Cheong5, Martin Gibert6, François Laviolette7, Bruce Gray8, Caroline Reinhold9, Mark Cicero10, Jaron Chong9, James Shaw11, Frank J Rybicki12, Casey Hurrell13, Emil Lee14, An Tang15.
Abstract
Artificial intelligence (AI) software that analyzes medical images is becoming increasingly prevalent. Unlike earlier generations of AI software, which relied on expert knowledge to identify imaging features, machine learning approaches automatically learn to recognize these features. However, the promise of accurate personalized medicine can only be fulfilled with access to large quantities of medical data from patients. This data could be used for purposes such as predicting disease, diagnosis, treatment optimization, and prognostication. Radiology is positioned to lead development and implementation of AI algorithms and to manage the associated ethical and legal challenges. This white paper from the Canadian Association of Radiologists provides a framework for study of the legal and ethical issues related to AI in medical imaging, related to patient data (privacy, confidentiality, ownership, and sharing); algorithms (levels of autonomy, liability, and jurisprudence); practice (best practices and current legal framework); and finally, opportunities in AI from the perspective of a universal health care system.Entities:
Keywords: Artificial intelligence; Ethics; Imaging; Legal; Machine learning; Radiology
Mesh:
Year: 2019 PMID: 30962048 DOI: 10.1016/j.carj.2019.03.001
Source DB: PubMed Journal: Can Assoc Radiol J ISSN: 0846-5371 Impact factor: 2.248