Work2023

A Comprehensive Review on the Use of Artificial Intelligence in Mental Health Care

40 citations0/0 files storedInternational Conference on Computing, Communication and Networking Technologies (ICCCNT)

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Digital Mental Health Interventions Artificial Intelligence in Healthcare and Education Artificial Intelligence in Healthcare

Abstract

The integration of Artificial Intelligence (AI) technology into mental health services has emerged as a promising way to improve the quality and accessibility of mental health services. In the field of analysis, Artificial Intelligence techniques like Natural Language Processing (NLP) and Machine Learning algorithms have shown great potential for the analysis of big data such as electronic medical records (EMRs) and social media posts. This technology can help identify patterns and symptoms of various mental disorders, facilitating early detection and intervention. AI also plays an important role in mental health diagnosis. Machine learning algorithms trained with extensive data enable accurate and efficient classification and diagnosis of different diseases. Additionally, AI-powered virtual assistants and chatbots are being developed to provide initial assessment, psychoeducation, and even treatment, thereby expanding access to mental health services. Healing is another area where intelligence shows promise. Intelligent algorithms can analyze patient information, past medical results, and evidence-based guidelines to recommend personalized treatments. In addition to this, techs such as VR-AR (virtual and augmented reality) and digital therapy combined with artificial intelligence are collaborating to provide treatment and improve the patient experience. AI programs continue to advance the care and management of mental health. Wearable devices equipped with sensors can collect physical and behavioural data in real-time, and this data can be analyzed by intelligent algorithms to detect early signs of relapse or flare-up. This proactive monitoring allows for timely intervention and personalized care, ultimately improving patient outcomes.

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References

2019Chatbots and Conversational Agents in Mental Health: A Review of the Psychiatric Landscape 2002Capturing momentary, self-report data: A proposal for reporting guidelines 2017Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning 2018Facebook language predicts depression in medical records

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