Digital Health Research and Health Data Pools

Schneider, Giulia

doi:10.1007/978-3-030-95427-7_2

Cited by 1 publication

(1 citation statement)

References 85 publications

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“…Privacy protection has become a hot topic in the health care AI research field [ 74 ], with numerous studies dedicated to developing innovative privacy-preserving solutions without compromising the performance of big data–driven AI models. These include developing privacy-enhancing technologies, such as homomorphic encryption [ 75 ], securing multiparty computation and differential privacy [ 76 ], and exploring new training methods and data governance models, such as distributed federated machine learning using synthesized data from multiple organizations [ 77 ], data-sharing pools [ 78 ], data trusts [ 79 ], and data cooperatives [ 80 ]. Second, the lack of clarity in accountability and regulation has also been universally identified in prior research as a key obstacle to the application of AI in health care [ 81 - 83 ].…”

Section: Discussionmentioning

confidence: 99%

Existing Barriers Faced by and Future Design Recommendations for Direct-to-Consumer Health Care Artificial Intelligence Apps: Scoping Review

He,

Zheng,

Ding

2023

J Med Internet Res

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Background Direct-to-consumer (DTC) health care artificial intelligence (AI) apps hold the potential to bridge the spatial and temporal disparities in health care resources, but they also come with individual and societal risks due to AI errors. Furthermore, the manner in which consumers interact directly with health care AI is reshaping traditional physician-patient relationships. However, the academic community lacks a systematic comprehension of the research overview for such apps. Objective This paper systematically delineated and analyzed the characteristics of included studies, identified existing barriers and design recommendations for DTC health care AI apps mentioned in the literature and also provided a reference for future design and development. Methods This scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines and was conducted according to Arksey and O’Malley’s 5-stage framework. Peer-reviewed papers on DTC health care AI apps published until March 27, 2023, in Web of Science, Scopus, the ACM Digital Library, IEEE Xplore, PubMed, and Google Scholar were included. The papers were analyzed using Braun and Clarke’s reflective thematic analysis approach. Results Of the 2898 papers retrieved, 32 (1.1%) covering this emerging field were included. The included papers were recently published (2018-2023), and most (23/32, 72%) were from developed countries. The medical field was mostly general practice (8/32, 25%). In terms of users and functionalities, some apps were designed solely for single-consumer groups (24/32, 75%), offering disease diagnosis (14/32, 44%), health self-management (8/32, 25%), and health care information inquiry (4/32, 13%). Other apps connected to physicians (5/32, 16%), family members (1/32, 3%), nursing staff (1/32, 3%), and health care departments (2/32, 6%), generally to alert these groups to abnormal conditions of consumer users. In addition, 8 barriers and 6 design recommendations related to DTC health care AI apps were identified. Some more subtle obstacles that are particularly worth noting and corresponding design recommendations in consumer-facing health care AI systems, including enhancing human-centered explainability, establishing calibrated trust and addressing overtrust, demonstrating empathy in AI, improving the specialization of consumer-grade products, and expanding the diversity of the test population, were further discussed. Conclusions The booming DTC health care AI apps present both risks and opportunities, which highlights the need to explore their current status. This paper systematically summarized and sorted the characteristics of the included studies, identified existing barriers faced by, and made future design recommendations for such apps. To the best of our knowledge, this is the first study to systematically summarize and categorize academic research on these apps. Future studies conducting the design and development of such systems could refer to the results of this study, which is crucial to improve the health care services provided by DTC health care AI apps.

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