Abstract
Emergence of health-related smartphone applications and their wide dissemination in public as well as healthcare practitioners has undergone criticism under the scope of public health. Still, despite methodological issues curbing the initial enthusiasm, availability, safety and, in certain cases, documented efficacy of these measures has secured regulatory approval. Bearing in mind these pitfalls, we describe the necessary steps towards implementation of deep learning techniques in the specific clinical context of women’s health and infertility in particular.
Highlights
We have come to an era where health information is readily available
We briefly describe the methodological weaknesses of apps targeting frequent clinical problems through a comprehensive literature search, and based on these, we propose specific key approaches integrating Artificial Intelligence (AI) into mobile apps in the specific clinical context of improving patient’s health
The Third Phase: External Validation of the New System (App+Artificial Neural Networks (ANNs)). This phase is necessary for the identification of issues that may arise from the use of the new system in a large scale, and includes: 1. Continuous external validation by other Medical Units and professionals in the field, allowing continuous re-adjustment of efficiency and stability of the ANN
Summary
We have come to an era where health information is readily available. Smartphone utilities demonstrate rapid advancement and the market offers applications (apps) covering almost every aspect of human life, accessible anywhere and anytime; these have engaged practitioners, providing handy tools for information access, monitoring, telemedicine and even supporting medical decisions [1,2,3,4,5,6,7]. E.g., type 1 & 2 diabetes [1,2], they have been proven effective and are currently recommended These benefits apply to patients/users, where interventions of notifying them have promoted disease monitoring, management, and health education. Few apps provide comprehensive information on all aspects [14], including effectiveness, side effects, and contraindications. Another issue is the discrepancy between expected and real-world user behaviors [15]. We briefly describe the methodological weaknesses of apps targeting frequent clinical problems through a comprehensive literature search, and based on these, we propose specific key approaches integrating AI into mobile apps in the specific clinical context of improving patient’s health
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