How AI Can Revolutionize Mental Health Support

There is increasing awareness and concern about mental health globally, amplified by the COVID-19 pandemic. India accounts for 15% of the world’s mental ailments burden, and the country is struggling with a substantial treatment gap that healthcare providers are working to bridge. While there are several explanations for this, social stigma is the most prevalent. This raises an important question: how will we ever fulfil this rising need, and can technology help?

From healthcare to education, artificial intelligence (AI) empowers important sectors, resolving issues and creating new impactful opportunities. The next big thing could be AI for mental wellness. Healthcare AI has the potential to lead a positive transformation in a sector that has long been ready for innovation thanks to the speed of technological development.

AI’s role in improving mental health

Many believe mental healthcare is a human-only profession, one in which emotional intelligence is crucial, as opposed to medical specialities like radiology or pathology, where AI can outperform doctors. Most mental health professionals are sceptical about AI, machine learning (ML) or natural language processing (NLP) to deliver empathic treatment. Robots might supplement conventional evaluation techniques, say researchers, but they are not equipped to replace mental healthcare experts. According to a Cambridge study, robots are more effective at identifying youngsters with mental health problems than parent- or child-reported tests.

Examples of how AI is revolutionising mental healthcare

1. Personalized treatment

AI is being used by businesses around the world to enhance customer service and provide new opportunities. In a segment like healthcare, AI is the ideal technology to handle the rising volume of data – especially in a nation like India, which can analyse enormous volumes of unstructured data. Healthcare professionals are using AI to expedite patient recovery, enhance the accuracy of medical analyses for individualised care and prevent illnesses through timely interventions.

2. Patient monitoring for early detection

Since the pandemic, there has been a rise in public awareness about the need for preventive healthcare, which has led to the popularisation of the internet of medical things. Physiological and behavioural data have been passively collected to aid in diagnosing various mental health conditions. Direct physiological data monitoring has, up until recently, proven difficult due to the size, cost, and mass of such equipment (e.g., heart rate, sleep quality, skin conductance). However, the emergence of various wearable gadgets, including wristbands, smartwatches, and fitness trackers, as well as the widespread use of smartphones, have made it easier to collect physiologically meaningful data. As a result, new technologies are being created quickly to support mental health. Earlier detection of severe disorders has enhanced the possibility of saving lives.

3. Conversational AI

Conversational AI delivers the one-on-one conversations that doctors desire for their patients. Conversational healthcare is quickly acknowledged as a way to provide the best treatment possible, customised to patient requirements. According to a survey by Market Research Future, over 52% of people use healthcare chatbots to obtain information, while about 36% of doctors agreed to use them to treat patients. Conversational AI-powered medical chatbots assist by reducing the number of hospital visits, unnecessary treatments and procedures, as well as hospital admissions and readmissions. For example, Wysa is an AI-based chatbot that can interpret user inputs after being educated with 100 million or so talks. For users with depression, stress, anxiety, sleep, and other mental health concerns, the app offers research-supported, commonly utilised approaches, including cognitive behavioural therapy (CBT), dialectical behaviour therapy (DBT), and meditation help.

4. Analyzing patient speech/behaviour

Healthcare businesses have already begun utilising AI technology to provide patients with accurate and up-to-date information without requiring them to visit hospitals. Few know that a person’s voice can vary somewhat every few milliseconds because of alterations in their body and medical problems. The voice characteristics that correspond to specific illness symptoms or changes in health can be determined using this large amount of information. We can train ML models to teach AI algorithms to recognise voice patterns typical of these patients using data collected from thousands of those who experience certain medical illnesses like anxiety and depression.

5. Detecting the need for mental health support

Unforeseen search results are rarely pleasant; sometimes, they can even be harmful and upsetting. Searchers can exclude direct results by using the Safe Search mode. But, occasionally, that may be just what a person is looking for. The AI-driven Multitask Unified Model, or MUM, from Google, can automatically and more precisely identify a broader spectrum of personal crisis queries, states Google in a blog. When a person is in need, MUM can more accurately determine the intentions behind their queries and choose when to present them with reliable and valuable information.

6. Emergency response

AI can assist human therapists but cannot replace them. It offers round-the-clock assistance and can act as an early warning system. Some apps, for example, detect warning signs of suicidal feelings during counsellor conversations and send in a 911 request. AI-augmented services may serve as a lifeline for individuals who reside in remote places where access to mental healthcare is limited or those unable to pay for frequent therapy sessions.

In India, which has several unorganised sectors, communities, languages and dialects, digitisation procedures are frequently inadequate and ambiguous and lack a national database for health records. As a result, the data for AI organisations may not accurately reflect a sizable portion of the population. However, AI-driven models have the power to gather and structure data to build a technology-driven ecosystem to empower mental healthcare professionals. This can enable healthcare professionals to better understand patient requirements by using AI and ML in care delivery and care management, empowering them to offer greater guidance and assistance for maintaining good mental health.

Views of the author are personal and do not necessarily represent the website’s views.

Dr. Jaideep Jesson Rayapudi, MBBS, MD is an aspiring cognitive neurophysiologist and expert in medical AI development. He currently serves as a Senior Subject Matter Expert in Healthcare AI at iMerit where he helps create large-scale datasets to power a variety of healthcare AI machine learning use cases. 

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The CSR Journal Team

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