Artificial Intelligence and Machine Learning-Based Medical Devices: A Products Liability Perspective

Conclusion Technological innovation outpaces the law, and artificial intelligence/machine learning is no different. Regardless of how legal doctrines evolve with the introduction of AI/ML-based products, until firm legal and regulatory guidelines progress, one thing is certain: There will be significant disagreement about how products liability law is applied. So, while these products present a new and lucrative market for manufacturers, the drive to supply an ever-increasing market demand must be balanced with a fulsome design, testing, and monitoring process. References Mitchell, Tom, Machine Learning Preface XV (1st ed. 1997). Sing, H., Meyer, A.N., and Thomas, E.J., "The Frequency of Diagnostic Errors in Outpatient Care: Estimations from Three Large Observational Studies Involving U.S. Adult Populations," BMJ Qual Saf, 2014. Smith-Bindman, R., "Use of Advanced Imaging Tests and the Not-So-Incidental Harms of Incidental Findings," JAMA Inter. Med., 2018. www.eyediagnosis.net/idx-dr https://www.medtronicdiabetes.com/products/sugar.iq-diabetes-assistant Goodfellow, I., Bengio, Y., and Courville, A., Deep Learning p. 2 (2016). Id., Deep Learning pp.440-441. IMDRF SaMD Working Group, “Software as a Medical Device”: Possible Framework for Risk Categorization and Corresponding Considerations, http://www.imdrf.org/docs/imdrf/final/technical/imdrf-tech-140918-samd-framework-risk-categorization-141013.docx. Id. U.S. Food & Drug Ad...
Source: MDDI - Category: Medical Devices Authors: Tags: Regulatory and Compliance Source Type: news