As technology develops, it is becoming more integrated within the health care sector for a variety of purposes. When health technologies are designed, several factors, such as purpose, complexity, and user autonomy, are considered. The development of sophisticated technologies causes creators to make certain assumptions about the ethical and social appropriateness of various machine functions (Lehoux et al., 2014). The high level of technological integration within health care places significant reliance on the medical process unto machines, with the assumption that they are more reliable. Machine learning, which is an evolving aspect of artificial intelligence, is a potentially revolutionary development in improving health care technology but requires an ethical set of standards to maintain the safety and privacy of patients.
The fundamental principle of machine learning is acquiring new data. Using the collected data, the machine can analyze it based on preprogrammed algorithms. The result, based on the machine function, can trigger a particular action. As the technology grows more sophisticated and complex, the capacity of machines is being expanded to more than a reactive action based on preprogrammed algorithmic functions. The focus is now on analyzing enormous amounts of data to find patterns and interactions which can be compared with previous data. Considering a machine can compute and analyze at a rate faster than any human, this aspect of learning helps to notice inconsistencies or predict patterns and outcomes quickly.
Currently, the aspect of machine learning is being used under human supervision, when data is being analyzed towards a specific outcome, such as predicting the possibility of various diseases based lab results or determining the side effects of medication based on previously recorded incidents. Unsupervised learning is currently an evolving process when the machine continues to constantly collect and analyze data based on a unified technological system within a facility. The machine begins to structure the data and draw conclusions based on deduction and patterns (Siwicki, 2017). It is a process that presents an overarching perspective on any organization or process that a human cannot identify from within. This evaluation can be used for recommendations on the improvement of care and management in a health care organization.
Machine learning is aimed towards aiding health care sector professionals to achieve goals in inpatient care delivery using precision medicine. AI can personalize and individually match recommendations based on patient history and collected medical data. Doctors can be informed of patient characteristics and individual traits, which can direct treatment and prediction of any complications. The overwhelming amount of manual processes within health care delivery creates the possibility of error that a machine can eliminate (Corbett, n.d.). As a result, the workflow process becomes extremely efficient and accurate that has an insurmountable number of benefits for a health care organization and quality of care, such as reduced costs and patient satisfaction.
Certain risks arise with the introduction of machine learning into health care. In order to rely on technology for medical purposes, it must demonstrate reliability in collection and analysis of data. Most “smart” technologies are still in early development, showing a lack of consistency, particularly in the collection of biometric data (i.e., heart rate measurements on wearable devices). In addition, since a tremendous amount of patient data is stored by the electronic systems, it is critical to ensure security for the protection of personal privacy (Hamid, 2016). Ethics and legislation often lag far behind rapidly evolving technology. It is necessary to develop ethical standards and metrics for evaluation of this specific type of health technology. As many processes are automated, people can be retrained towards ethical management and guidance of artificial intelligence (Miliard, 2017).
It is evident, that both reliability and security of machine learning are still inconsistent, creating difficulties for full implementation within the health care sector. The rapid evolution of machine learning will produce significant improvements in the next decade. With a proper set of ethical standards for development, implementation, and protection of this technology, the health care sector would be willing to trust medical AI.
References
Corbett, E. (n.d.). The real-world benefits of machine learning in healthcare. Web.
Hamid, S. (2016). The opportunities and risks of artificial intelligence in medicine and healthcare. Web.
Lehoux, P., Gauthier, P., Williams-Jones, B., Miller, F. A., Fishman, J. R., Hivon, M., & Vachon, P. (2014). Examining the ethical and social issues of health technology design through the public appraisal of prospective scenarios: A study protocol describing a multimedia-based deliberative method. Implementation Science, 9(81), 1-15. Web.
Miliard, M. (2017). As AI spreads through healthcare, ethical questions arise. Web.
Siwicki, B. (2017). Machine learning 101: The healthcare opportunities are endless. Web.