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Five ways AI is Changing the Game for Mental Health Diagnosis and Treatment

Artificial Intelligence (AI) in mental health diagnosis and treatment has rapidly transitioned from a speculative concept to a tangible reality. This evolution is no sci-fi pipe dream; independent, peer-reviewed studies back its efficacy in more accurate identification, reliable assessment, and easier monitoring of conditions of anxiety and depression.

And its arrival comes none too soon. World Health Organization member states adopted the

Comprehensive Mental Health Action Plan as far back as 2013, and the COVID-19 pandemic

deafening alarm bells call for a radical change in the accuracy, speed, and accessibility of

mental health diagnosis, and the efficiency and efficacy of treatment.

At the same time, the tech world had taken machine learning and artificial intelligence to a point where they could find a vital niche. Landmark studies have already backed up the utility of these cutting-edge technologies in the mental health sphere.

Here, we’re going to look at five key areas where AI is enhancing patient care and streamlining the work of mental health professionals.

1. AI-powered predictive diagnostics

Able to analyze vast swathes of data, make insightful connections, and produce early warning signs, AI and machine learning have allowed healthcare providers to deal with mental health issues in advance, rather than fighting an uphill battle to undo crises that are already underway. Early detection in mental health is of paramount importance in averting serious cases of depression and suicide, and AI is proving to be a valuable asset in spotting trends across a range of disparate indicators.

For example, Ellipsis Health harnesses speech patterns that signal mental health issues even if the speaker is unwilling or unable to self-report. Crucially, we incorporate acoustics and

semantics, bolstering assessment with another layer of indicators. These objective biomarkers are an essential tool in a care team’s arsenal, allowing them to intervene with treatment before a patient’s mental state becomes more serious.

2. Customized treatment plans

In addition to AI’s innovations in the detection phase, those same vast databases can help to

offer personalized treatment plans that combine global trends with a patient’s personal medical history. By synthesizing vocal biomarkers, patient medical histories, behavioral patterns, and responses to past treatments in an instant, artificial intelligence can curate plans that treat patients on an individual level, rather than offering a one-size-fits-all solution to unique situations.

This allows psychiatrists to go beyond traditional patient health questionnaires (PHQ) and

generalized anxiety (GAD) survey results and instead gain objective measurements from patient speech. The result is a more nuanced and effective treatment plan. For instance, if a

patient’s vocal biomarker data coincide with others who have responded well to cognitive-

behavioral therapy (CBT), the AI system can prioritize this in their treatment plan.

3. Virtual mental health assistants

There is a vital need for speed, accessibility, and engagement in mental health treatment, and AI-powered virtual mental health assistants make a strong claim for providing a solution. One way healthcare providers are beginning to leverage AI is to conduct initial screening. These short, easy assessments generate data that informs clinicians ahead of an in-person appointment and fast-track any serious cases, such as patients with severe depression.

At Ellipsis Health, we make our technology as accessible as possible through a mobile app,

which gives patients 24/7 access from anywhere. Ellipsis Health’s tech integrates seamlessly

with insurance provider apps and websites and maps the output to standard PHQ and GAD


4. More accurate and effective monitoring

Implementing AI in mental health monitoring provides healthcare providers with a vast array of data points over time, giving a clearer picture of a patient’s state more easily than in-person assessments. Aside from increasing a clinician’s understanding, a secondary benefit is that patients feel a part of the process, which can be motivating and reassuring.

The use of AI tools in therapy also helps in addressing one of the significant challenges in

mental health care: the subjective nature of self-reported symptoms. Ellipsis Health’s

objective analysis provides a more accurate picture of a patient’s mental state at any given

moment. Moreover, the system continuously learns as its database increases, adapting to

speech patterns and increasing accuracy in identifying changes that could signify mental health issues.

5. AI in mental health research and knowledge discovery

From a research standpoint, AI is already playing a pivotal role in advancing our understanding of mental health conditions. Its capacity to process and analyze vast quantities of data from diverse sources, from clinical records and patient interactions to research studies and conference talks, has accelerated knowledge discovery in the field.

At Ellipsis Health, we’ve drawn on these breakthrough techniques to shed light on each step of our process. From evaluating the utility of deep language models and transfer learning to finding new methods to analyze different languages, machine learning, and artificial intelligence techniques allow us to accelerate crucial advances in detecting depression and anxiety.

The next steps for AI in Mental Health

Looking forward, it is clear that AI in mental healthcare is still in its infancy. As datasets grow in size and reliability, we will unlock a wave of new applications to make assessment and

treatment more effective for a greater number of people. We are proud to be already riding this wave, with the largest corpus of published scientific papers from a single company linking machine learning algorithms to clinical depression and anxiety.

As a taster, below are some Ellipsis Health pending patents, in addition to our already-patented systems and methods for mental health assessment:

● Confidence evaluation to measure trust in behavioral health survey results

● Acoustic and natural language processing models for speech-based screening and

monitoring of behavioral health conditions

● Predicting behavioral and mental health conditions from speech using multi-language


But with great power comes great responsibility, and data security must be at the forefront of discussions around AI in mental health. As HIPAA, GDPR, and SOC2 compliant, we follow the same privacy and security guidelines used by physicians and hospitals. On a more micro scale, we process each voice data file to remove both personal health information as well as

personally identifiable information to protect privacy.

For more information on Ellipsis Health and their groundbreaking work in AI and mental health, follow us on LinkedIn where we share emerging trends and discoveries, as well as the real-life benefits they bring to patients.


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