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Effective Benefits Of Chatbots In Mental Health - ONPASSIVE

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Chatbots are becoming a big part of the world. With a chatbot, you can have information at your fingertips with no help and hassle of having to search for it. Mental health is vital to many, and so are the people that talk to you about it – but with these benefits, it might be worth using a chatbot! This article discusses the advantages and disadvantages of using chatbots for mental health and how they can help you deliver care more efficiently and effectively. A chatbot is a computer program that can interact with people in a virtual environment.


Diagnosing Mental Health Disorders Through AI Facial Expression Evaluation

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Researchers from Germany have developed a method for identifying mental disorders based on facial expressions interpreted by computer vision. The new approach can not only distinguish between unaffected and affected subjects, but can also correctly distinguish depression from schizophrenia, as well as the degree to which the patient is currently affected by the disease. The researchers have provided a composite image that represents the control group for their tests (on the left in the image below) and the patients who are suffering from mental disorders (right). Individuals with affective disorders tend to have raised eyebrows, leaden gazes, swollen faces and hang-dog mouth expressions. To protect patient privacy, these composite images are the only ones made available in support of the new work.


How AI Can Help Mental Health

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In today's world, the need for mental health resources is urgent. Globally, in addition to great need for mental health services, there is simultaneously a shortage of mental health providers. As we know, AI refers to the concept of computers solving problems on their own. What better way to help us solve this problem and create more resources than with AI? In fact, artificial intelligence is making advancements in many exciting areas of mental health.


Chatbots in Healthcare [All you need to know and best use cases 2022]

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Among all sectors, those AI assistants have the same benefits overall. Chatbots are amazing for delivering excellent customer support and make you spend less money on staff and invest in other areas. Also can work as an excellent marketing tool. But since 2020 and COVID hit, the healthcare sector has had a lot of changes, and chatbots helped patients to get quick medical information and know about nearby medical facilities. In 2022, chatbots are still an amazing tool because of machine learning and how they can work as an "AI doctor". Let's dive in-depth into why chatbots are an awesome tool for healthcare.


Data-driven mental healthcare solving the crisis? - Information Age

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Nick Ismail One in four people have mental health issues and in men under 50, suicide is the main cause of death. It is a huge, often misunderstood problem that pervades every society and most families. This was the opening message delivered by Valentin Tablan, an artificial intelligence expert and scientist at Ieso Digital Health, at the Healthcare Summit in London last month. His message was clear: the mental health problem is endemic, but we know the solution in cognitive behavioural therapy. The issue is not everyone has access to this service. There are not enough therapists to deliver the necessary therapy.


Artificial Intelligence could be the future of mental illness detection

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In order to prevent and cure debilitating ailments including Alzheimer's disease, schizophrenia, and autism, a new study at Georgia State University's TReNDS Center may result in early detection. This study was published in the Journal of Scientific Reports.


Top Artificial Intelligence (AI) Powered Health Apps in 2022

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In the context of healthcare, artificial intelligence (AI) refers to the use of sophisticated algorithms and software to simulate human cognition in the analysis, interpretation, and comprehension of complex medical and healthcare data. The top 10 AI healthcare companies are likely to dominate the global market for AI in healthcare, which is anticipated to reach US$ 28 billion in 2025. Around 100 different AI development tools were approved for medical usage in 2020. The most common specializations are hematology, cardiology, and radiology. Over US$4.6 billion will be spent on medical surgical robot use in 2020.


Finding hard-to-find patients: Integrating real-world data and AI

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Identifying patients'outside the clinic' can provide significant benefits for researchers and population health managers. Better understanding of patient cohorts can shed light on patient journeys, help optimize decisions around treatment choice and timing and inform the development of new therapies and intervention programs. Even without'in-clinic' insight - the ability to directly interact with the patient, to confirm their membership in a cohort - we have plenty of population-level tools we can use to try to identify patients. Even so, patient identification in many cases can become a challenging, even impossible task. For example, we may want to find a cohort with a certain health condition: in the simplest circumstances, if the condition is well-documented in structured medical records, we can query large real-world datasets to isolate the cohort using standardized coding.


Novel 'Fuzzy' AI Algorithms to Help Patients with Memory Loss

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Like our brains, a new computer program created by Parham Aarabi of the University of Toronto can store and retrieve information strategically. An experimental tool that uses the novel algorithm to aid those with memory loss has also been developed by the associate professor in the Faculty of Applied Science & Engineering's Edward S. Rogers Sr. department of electrical and computer engineering. In the minds of most people, AI is more robotic than humans, according to Aarabi, whose approach is examined in a paper presented at the IEEE Engineering in Medicine and Biology Society Conference in Glasgow. Aarabi believes it should change. Computers have traditionally needed explicit instructions from their users on what data to save.


Machine Learning May Help Identify Handgun Purchasers Who Are at High Risk of Suicide

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The latest Tweet by IANS India states, 'Machine learning may help identify handgun purchasers who are at high risk of suicide as it identifies …