The Role AI in Supporting Medical Data Field with Machine Learning Technique

Abstract

Artificial Intelligence (AI) has great potential to improve population health overall and transform the way healthcare is delivered. But integrating AI into healthcare comes with dangers that should be carefully considered to avoid unforeseen consequences. In response to this worry, the High-Level Expert Sort on AI of the European Commission has published extensive ethics guidelines towards “trustworthy” AI, providing practitioners and stakeholders with insightful information to guarantee moral and reliable AI applications. The emerging discipline of AI ethics studies has switched its focus from formulating abstract principles into practical suggestions as a reply to the European Commission’s initiatives. Interestingly, trustworthy AI is interpreted, relevant, and used differently in different situations and domains. This work contributes by illustrating how the general AI the HLEG, trustworthy AI standards may be used in the healthcare industry. A case study is provided that assesses how machine learning is now used in Copenhagen, Denmark, to assist in identifying cardiac arrest during emergency calls. An multidisciplinary team of the thinkers, administrators, social scientists, technological, legal, and medical professionals performed the evaluation. They used Z-Inspection ${ }^{\circledR}$ to uncover particular ethical trade-offs and obstacles related to practical uses of AI in healthcare. This multidisciplinary approach seeks to clarify intricate trade-offs and highlights the need for careful human scrutiny when dealing with the socio-technical elements of AI in care.

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