인공지능진료시스템 수용에 관한 연구 : 의료기관 종사자와 일반인의 기대 차이를 중심으로
Study on the Acceptance of Artificial Intelligence Diagnosis Systems: Expectation Gaps Between the General Public and Staff of Medical Institutions
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Objectives: This study investigates the expectation gap between the general public and staff of medical institutions based on the introduction of the artificial intelligence diagnosis systems at medical institutions. Methods: To this end, the study analyzes 334 data sets collected nationwide through online investigations. Results: The results of the analysis reveal that, in all areas, except for the cost-concern area, the general public has higher expectations regarding artificial intelligence diagnosis systems than staff working at medical institutions. Despite the cost concerns, the general public perceive the positive aspects of the artificial intelligence diagnosis systems. The results confirm the presence of gaps between the staff and general public in terms of perception and emotion. Conclusions: These findings can help in the design of an efficient system for managing customer responses and staff’s decision-making regarding the introduction of new technology and can extend the technology acceptance theory at medical institutions.
Abstract
Objectives: This study investigates the expectation gap between the general public and staff of medical institutions based on the introduction of the artificial intelligence diagnosis systems at medical institutions. Methods: To this end, the study analyzes 334 data sets collected nationwide through online investigations. Results: The results of the analysis reveal that, in all areas, except for the cost-concern area, the general public has higher expectations regarding artificial intelligence diagnosis systems than staff working at medical institutions. Despite the cost concerns, the general public perceive the positive aspects of the artificial intelligence diagnosis systems. The results confirm the presence of gaps between the staff and general public in terms of perception and emotion. Conclusions: These findings can help in the design of an efficient system for managing customer responses and staff’s decision-making regarding the introduction of new technology and can extend the technology acceptance theory at medical institutions.
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