Iván Otero González

Artificial intelligence (AI) and innovative technology have the potential to greatly assist healthcare professionals in identifying and addressing mental health issues. Some of the ways AI and technology can help include:

In conclusion, AI and innovative technology have the potential to greatly improve the ability of healthcare professionals to identify, monitor, and treat mental health issues, and to provide better care to patients in need.

Thanks for your answer Janak Trivedi

Let me share with all the answer of chatGPT to the question:

In which way could the Artificial Intelligence and the innovative technology help to the health staff to identify the great amount of mental health?

Artificial intelligence (AI) and innovative technology have the potential to greatly aid health staff in identifying and addressing mental health issues. Here are a few ways this can be achieved:

It is important to note that while AI and innovative technology have the potential to greatly aid health staff in identifying and addressing mental health issues, they should not be viewed as a replacement for human care and compassion. The human touch remains a critical component in the delivery of mental health care.

Really well answered

Objectivation of diagnostic models in mental health, i.e. quantifying mental health via mood science=offering alternative pathways for behavioral and cognitive treatment.

Thanks Aparna Sathya Murthy , the Hashing looks good. I honestly never used it.

I would like to give a glance to this paper. It would be a very good opportunity to give a chance to this kind of methodoly.

I am now focused to solve the screening of depression disorders.

Best regards,

Thanks Stephen I. Ternyik . I guess you make the screening of people with depression disorder throw different kinds of mood science tests, don't you? And is there a tree of these pathways registered in some protocol or manual?

Best regards.

Yes, Iván Otero González Just take a look of our free works on quantifying mental health (QMH/Ternyik & Fermelia) in the net.

Dear sir,

Download the Aidos-x system for free from the site http://lc.kubagro.ru/aidos/_Aidos-X.htm . It is an excellent intelligent system for diagnosing and classifying human diseases.

Methods for using the system Aidos-x for diagnosing human diseases are disclosed in lectures with sound "Using automated system-cognitive analysis for the classification of human organ tumors", "Intelligent system for diagnosing early stages of chronic kidney disease", which can be downloaded right now from the website https://www.patreon.com/user?u=87599532 After subscribing to this site, you will receive databases for medical research to identify those diseases that you will read about in lectures. The acquired skills in working in the system Aidos-x will allow you to apply for grants to perform scientific research in medicine.

Sincerely, Vladimir Ryabtsev, Doctor of Technical Sciences, Professor of Information Technology

Thanks Akim Onoke . I've neve heart about the Aidos-X system and it could be a new World for me at he e-health. I will review in depth to take advantage of your advice. Thanks so much.

Dear Iván Otero González,

My lectures are useful for graduate students and doctoral students for the preparation of dissertations and scientific papers. Based on the results of research conducted with the help of the Aidos-x system, 5 doctoral and 6 master's theses were defended in economic, technical, psychological and medical sciences. The materials of the lectures will help the heads of centers for innovative development of medical faculties of universities to apply the Aidos-x system for diagnosing human diseases. For the implementation of scientific projects, you can get as many grants as there are types of diseases in people, which are even difficult to calculate. To complete the projects, it is necessary to develop training datasets, which is a very long and laborious process. To determine the risk of stroke, I used a dataset that is a 1000*10 matrix. It took 30 seconds to fix one matrix cell, and 5000 minutes or 83 hours to debug the entire matrix array. Therefore, it is recommended to use the representative training databases that I have already developed for detecting diabetes, early kidney disease, liver disease, lung cancer, spinal diseases, detecting tumors in human organs and the risk of liver implant failure, which I will send free of charge to subscribers of the site after downloading the corresponding lecture. Please tell me, for which human diseases would you like to receive a training database in the first place?

Thanks Stephen I. Ternyik I guess you are thinking for screening of general disorders. But I find it a real challenge to screen of people with or without depression. I am particularly interested on this screening.

by increasing the reliability of diagnostics in comparison with natural intelligence

Actually, in a variety of ways, but despite these tools, human interaction and care are still needed, but AI rather complements and enhances the work of healthcare staff.

● Early detection: AI-powered tools can analyze patterns in patient data to detect mental health issues earlier than traditional methods. For example, machine learning algorithms can analyze social media activity, voice tone, and facial expressions to detect signs of depression, anxiety, or other mental health issues.

● Personalized treatment: AI-powered tools can analyze patient data and provide personalized treatment plans based on the patient's individual needs. For example, chatbots and virtual assistants can provide emotional support, cognitive-behavioral therapy, and other interventions tailored to each patient's unique circumstances.

● Remote monitoring: AI-powered tools can help healthcare staff monitor patients remotely and provide proactive interventions when needed. For example, wearable devices can track physical activity, sleep patterns, and other health metrics, providing early warning signs of potential mental health issues.

● Data analysis: AI-powered tools can analyze large amounts of patient data to identify patterns and trends in mental health issues. This data can help healthcare staff identify risk factors, develop more effective treatment plans, and improve patient outcomes.