First, do no harm. Ethical and legal issues of artificial intelligence and machine learning in veterinary radiology and radiation oncology.
Eli B CohenIra K GordonPublished in: Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association (2022)
Artificial Intelligence and machine learning are novel technologies that will change the way veterinary medicine is practiced. Exactly how this change will occur is yet to be determined, and, as is the nature with disruptive technologies, will be difficult to predict. Ushering in this new tool in a conscientious way will require knowledge of the terminology and types of AI as well as forward thinking regarding the ethical and legal implications within the profession. Developers as well as end users will need to consider the ethical and legal components alongside functional creation of algorithms in order to foster acceptance and adoption, and most importantly to prevent patient harm. There are key differences in deployment of these technologies in veterinary medicine relative to human healthcare, namely our ability to perform euthanasia, and the lack of regulatory validation to bring these technologies to market. These differences along with others create a much different landscape than AI use in human medicine, and necessitate proactive planning in order to prevent catastrophic outcomes, encourage development and adoption, and protect the profession from unnecessary liability. The authors offer that deploying these technologies prior to considering the larger ethical and legal implications and without stringent validation is putting the AI cart before the horse, and risks putting patients and the profession in harm's way.
Keyphrases
- artificial intelligence
- machine learning
- big data
- deep learning
- healthcare
- endothelial cells
- decision making
- end stage renal disease
- induced pluripotent stem cells
- ejection fraction
- chronic kidney disease
- prognostic factors
- adipose tissue
- electronic health record
- transcription factor
- peritoneal dialysis
- risk assessment
- health information
- glycemic control