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Tag: personalization

This AI Chatbot Has Helped Doctors Treat 3 Million People–And May Be Coming To A Hospital Near You

Article Excerpt: The problem with turning to the internet for medical advice is that you can come away thinking that you either have a headache or a brain tumor – but the reality is you probably just have a headache. With K Health, Allon Bloch is creating an antidote to “Dr. Google” that ingests your symptoms and medical history via an AI-powered chatbot, sifts through the data of millions of patients and suggests a medical condition based on how you compare to other people like you. “We’re trying to mimic the best doctor in the world,” says Bloch, 53, cofounder and CEO of the seven-year-old New York-based startup.

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Article Source: Forbes


AI In Mental Health: Opportunities And Challenges In Developing Intelligent Digital Therapies

Article Excerpt: Clinicians, therapists, and researchers are increasingly finding that artificial intelligence (AI) can be a powerful tool in the provision of mental healthcare. As I will cover in this article, a growing body of evidence suggests that AI can help with diagnosing conditions, developing therapies, and enabling more personalized approaches and treatments.

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Article Source: Forbes


Personalization strategies in digital mental health interventions: a systematic review and conceptual framework for depressive symptoms

Hornstein S, Zantvoort K, Lueken U, Funk B, Hilbert K. (2023). Personalization strategies in digital mental health interventions: a systematic review and conceptual framework for depressive symptoms. Front. Digit. Health 5:1170002. doi: 10.3389/fdgth.2023.1170002

The aim of this systematic literature review was to explore how personalization is utilized and what benefits it has for digital mental health interventions (DMHIs). The search included empirical studies on DMHIs targeting depression in adults from 2015 to 2022, which resulted in 138 articles and 94 distinct DMHIs. Personalization was operationalized as purposefully designed variation between individuals in an intervention’s therapeutic elements or its structure. Furthermore, personalization strategies were differentiated by what is personalized (i.e., content, level of guidance, content order) and the mechanism (i.e., user choice, provider choice, machine learning). Applying this definition, researchers identified personalization in 66% of the interventions included, with personalized intervention content (32%) and communication with the end user (30%) as most prevalent. Personalization via automated if-then-decision rules (48%) and user choice (36%) were the most commonly used mechanisms, while application of machine learning was rare (3%). Overall, this review demonstrated that the majority of DMHIs for depression use personalization approaches; however, future interventions could provide tailoring on more dimensions of the intervention experience and may benefit from using machine learning models. Additionally, empirical evidence for the benefits of personalization was scarce and further evidence is needed.