Machine learning (ML) is increasingly being used by doctors and hospitals to make important health care decisions. ML can harm patients if it is inaccurate or biased. However, ML cannot be evaluated or regulated easily because its algorithms “learn” and are constantly evolving. ML development now involves players that are new to health care and thus may not recognize the ethical issues or pitfalls of ML in medicine. The research team seeks to identify potential barriers to development of safe and ethical ML.
Identifying Potential Barriers to and Enablers of Development of Ethical Machine Learning for Health Care
Ariadne A. Nichol et al., A Typology of Existing Machine Learning–Based Predictive Analytic Tools Focused on Reducing Costs and Improving Quality in Health Care: Systematic Search and Content Analysis, Journal of Medical Internet Research, June 2021Read more