It is challenging for programmers to build a mobile health app that is rich in AI features, and near impossible for non-technical users such as domain experts and patients. However, it is exactly these users that possess the domain knowledge and experience on how to best manage health conditions, and how AI features can help achieve that goal. End-user development environments, such as MIT Punya, can help lay users to better collaborate on mobile health apps; and even open the door for these users, given some training, to prototype their own mobile health apps. As a subfield of AI, Semantic Web technology can help with integrating online data sources with patient health data, and reasoning over the integrated data to issue smart health recommendations.

Development of AI-Enabled Apps by Patients and Domain Experts Using the Punya Platform: A Case Study for Diabetes

Loseto, Giuseppe;
2022

Abstract

It is challenging for programmers to build a mobile health app that is rich in AI features, and near impossible for non-technical users such as domain experts and patients. However, it is exactly these users that possess the domain knowledge and experience on how to best manage health conditions, and how AI features can help achieve that goal. End-user development environments, such as MIT Punya, can help lay users to better collaborate on mobile health apps; and even open the door for these users, given some training, to prototype their own mobile health apps. As a subfield of AI, Semantic Web technology can help with integrating online data sources with patient health data, and reasoning over the integrated data to issue smart health recommendations.
978-3-031-09341-8
978-3-031-09342-5
Patient apps, Diabetes management, Semantic web
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12572/10146
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