Stakeholders’ Requirements for Artificial Intelligence for Healthcare in Korea
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Objectives: The outlook of artificial intelligence for healthcare (AI4H) is promising. However, no studies have yet discussedthe issues from the perspective of stakeholders in Korea. This research aimed to identify stakeholders’ requirements forAI4H to accelerate the business and research of AI4H. Methods: We identified research funding trends from the KoreanNational Science and Technology Knowledge Information Service (NTIS) from 2015 and 2019 using “healthcare AI” and relatedkeywords. Furthermore, we conducted an online survey with members of the Korean Society of Artificial Intelligence inMedicine to identify experts’ opinions regarding the development of AI4H. Finally, expert interviews were conducted with 13experts in three areas (hospitals, industry, and academia). Results: We found 160 related projects from the NTIS. The majordata type was radiology images (59.4%). Dermatology-related diseases received the most funding, followed by pulmonarydiseases. Based on the survey responses, radiology images (23.9%) were the most demanding data type. Over half of the solutionswere related to diagnosis (33.3%) or prognosis prediction (31%). In the expert interviews, all experts mentioned healthcaredata for AI solutions as a major issue. Experts in the industrial field mainly mentioned regulations, practical efficacyevaluation, and data accessibility. Conclusions: We identified technology, regulatory, and data issues for practical AI4H applicationsfrom the perspectives of stakeholders in hospitals, industry, and academia in Korea. We found issues and requirements,including regulations, data utilization, reimbursement, and human resource development, that should be addressed topromote further research in AI4H.
Abstract
Objectives: The outlook of artificial intelligence for healthcare (AI4H) is promising. However, no studies have yet discussedthe issues from the perspective of stakeholders in Korea. This research aimed to identify stakeholders’ requirements forAI4H to accelerate the business and research of AI4H. Methods: We identified research funding trends from the KoreanNational Science and Technology Knowledge Information Service (NTIS) from 2015 and 2019 using “healthcare AI” and relatedkeywords. Furthermore, we conducted an online survey with members of the Korean Society of Artificial Intelligence inMedicine to identify experts’ opinions regarding the development of AI4H. Finally, expert interviews were conducted with 13experts in three areas (hospitals, industry, and academia). Results: We found 160 related projects from the NTIS. The majordata type was radiology images (59.4%). Dermatology-related diseases received the most funding, followed by pulmonarydiseases. Based on the survey responses, radiology images (23.9%) were the most demanding data type. Over half of the solutionswere related to diagnosis (33.3%) or prognosis prediction (31%). In the expert interviews, all experts mentioned healthcaredata for AI solutions as a major issue. Experts in the industrial field mainly mentioned regulations, practical efficacyevaluation, and data accessibility. Conclusions: We identified technology, regulatory, and data issues for practical AI4H applicationsfrom the perspectives of stakeholders in hospitals, industry, and academia in Korea. We found issues and requirements,including regulations, data utilization, reimbursement, and human resource development, that should be addressed topromote further research in AI4H.
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