"Application of Artificial Intelligence in the Health Care Safety Context: Opportunities and Challenges"
There is a growing awareness that artificial intelligence (AI) has been used in the analysis of complicated and big data to provide outputs without human input in various health care contexts, such as bioinformatics, genomics, and image analysis. Although this technology can provide opportunities in diagnosis and treatment processes, there still may be challenges and pitfalls related to various safety concerns. To shed light on such opportunities and challenges, this article reviews AI in health care along with its implication for safety. To provide safer technology through AI, this study shows that safe design, safety reserves, safe fail, and procedural safeguards are key strategies, whereas cost, risk, and uncertainty should be identified for all potential technical systems. It is also suggested that clear guidance and protocols should be identified and shared with all stakeholders to develop and adopt safer AI applications in the health care context.
In health care, AI is defined as the mimicking of human cognitive functions by computers. AI has been inspiredby the functioning of biological neurons and includes the basics of sensing, recognition, and object recognition to enable machines to perform as good as or even better than humans. However, with the inherent lack of articulation and generation of insights, AI cannot replace physicians in health care.With no universally applicable rules in health care, AI must be supplemented with physician judgment in many instances. An extensive correlation of history and clinical findings is needed for the diagnosis or monitoring of any disease state. The physician–patient relationship is guided by associative and lateral thinking and can influence management decisions. Moreover, the influence of several factors (eg, psychosocial, emotional)on disease outcomes falls outside the scope of AI. Machines can be more precise, reliable, and comprehensive and have relatively lower risk of bias; however, they still lack the elements of trust and empathy.There is a growing concern that AI systems learn by doing and, with repeat training, can outperform humans. AI holds a promising future in health care but only when used with diligence for the right purposes. (p.2)
KeywordsArtificial Intelligence, Machine Learning, Health Care Work, Worker Replacement
ThemesUnemployment, AI and Medicine, AI and Healthcare
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