How AI gives us a new understanding of mental health
When it comes to mental health, AI cannot only help us to make existing systems better and more efficient but help us unlocking a new frontier of knowledge.
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Often when we speak about applying AI to specific industries it’s about automating workflows and using our current understanding to make things more efficient. This is different in the mental health space. AI here has the potential to bring our understanding of the human mind and the science of mental health to a completely new level.
Mental health diagnosis, so far, relies solemnly on self reports and occasional meetings with clinicians. Besides drug therapy we treat patients mostly with talk therapy employing a vast array of different approaches from hypnosis to Cognitive Behavioral Therapy. However, we have little evidence on what actually works. If a patient's condition gets better we rarely know whether this was due to the therapeutic approach or because of the therapist's personality or because they changed their diet or got better sleep.
Although we know about various factors correlated with mental health we do not know much about the causalities and individual differences. Which behavioral pattern contributes to an individual's mental health? And how much so? What kind of therapy with what kind of therapist works for whom? Which commonly used practices might harm certain individuals? All of these questions require answers if we want to really understand mental health. But so far we can merely guess at the answers.
With the advent of artificial intelligence, however, we might have found the tool that is for the science of mental health what telescope and calculus were for physics. With new mental health technology we will be able to collect and integrate data from previously untapped sources like behavioral data from our digital devices or sleep and general health data, collected by wearables.
These AI tools hold the promise of decoding the intricate neural networks of the mind, offering a more profound understanding of mental processes and health. This has the potential to be not only a revolution for mental health patients but also a scientific revolution introducing AI as the new paradigm for mental health research.
"Mental health has the potential for a revolution, but its realization hinges on our immediate action." says Amruth Ravindranath, Neuroscientist and founder of Addera Health. Amruth helped me to develop a better understanding of how AI can revolutionize Mental Health and how our action plan looks like. I am sharing these insights as well as what the next step looks like.
Step 0: Tackling the mental health crisis
Before we can realize this revolution however, we must address the immediate problems our mental health care system has right now. By building technology to make mental health care better, we establish the infrastructure and collect the data necessary to advance our understanding.
Here are the main problems in mental health care right now:
Access to care: Access remains a substantial barrier in mental health care. Geographic isolation, financial constraints, and social prejudices often impede individuals from seeking help. Even in prosperous nations, a shocking number of people can’t access the care they desperately need, leading to untreated conditions and escalating crises. Since we heavily rely on 1-1 counseling, we can’t easily scale the care we provide to people. Additionally we provide almost no care to people with mental health issues below the clinical threshold.
Quality of Care: Once access is secured, the disparity in care quality becomes evident. Despite the presence of highly trained professionals in the field, a uniform standard of care is elusive. Practices vary widely and the lack of personalized treatment plans means that individuals often receive generic care, which may not adequately address their specific needs.
Mental health tech as a proposed solution to these problems is not new. It has been around for quite a while without contributing much. However, times are different now. Multiple trends in technology as well as in society are coming together creating unprecedented momentum:
(Generative) AI: Current workflows in the mental health space are mostly language based. There are many other data points, like general health data, that do not get factored in the decision making so far. So, we’ve got vast amounts of unstructured data, mostly in the form of language. Situations like these are ripe for AI . The boom we’ve experienced with this technology over the last months creates a unique opportunity for mental health tech.
Awareness: The recognition that we are in a mental health crisis has been rising for years, but the COVID 19 pandemic really highlighted the deep problems we have. While primarily a health crisis, the pandemic inadvertently spotlighted another pervasive issue – the state of global mental health. The challenges it unveiled, though not new, are clearer than ever before. Although stigma is still an issue in many social groups, the new awareness opens up new discussions and new opportunities to solve the underlying problems.
Adjacent tech trends: We’ve seen a massive trend toward data driven and personalized medicine. It’s more and more common to track health data via wearables. Over the next few years we will see more opportunities to leverage this data for various health applications. This presents mental health tech with the unique opportunity to incorporate new data sources
As our technological capabilities rise, our moral responsibility towards those in need rises as well.
In our pursuit to address the mental health crisis, integrating technology like AI can be invaluable. It can help bridge gaps, refine practices, and ensure that as our understanding of mental health evolves, our approach to addressing it evolves too.
Step 1: What needs to be done now
Let us now get more concrete and look at the specific applications that can be built. It's useful here to differentiate between two primary user groups: individuals with clinically diagnosed mental health conditions and those experiencing subclinical mental health issues.
Despite the overlap in their needs, each group necessitates distinct features from the mental health tools we develop. Importantly, these tools are subject to different regulatory and safety standards. AI applications designed for clinically diagnosed patients must adhere to stringent clinical validation and regulatory compliance, given that they are part of formal treatment plans. On the other hand, AI tools for subclinical support are generally subject to less rigorous oversight, but still require attention to privacy, data security, and ethical considerations to ensure they provide safe and effective guidance.
For clinically diagnosed patients, AI can enable more precise predictive analyses and personalized treatment plans. For instance, by examining patterns in large datasets, AI can identify individuals at higher risk of adverse outcomes, allowing for earlier and more targeted interventions. It also supports clinicians in tailoring treatment plans to individual needs by analyzing data from similar cases, which can lead to more effective management of conditions. Moreover, AI-enhanced monitoring tools can provide clinicians with real-time feedback on patient progress, ensuring that treatment remains dynamic and responsive to the patient’s evolving condition.
For those grappling with subclinical mental health concerns, AI offers a less intimidating and more accessible form of support. Mental health apps infused with AI provide a suite of tools for self-management, from mood tracking to therapeutic exercises, making mental health care proactive and personalized. AI chatbots act as a first line of emotional support, offering conversational assistance that can guide users through difficult moments or towards professional help if necessary. This level of immediate and personalized support can be critical in preventing subclinical issues from escalating into more severe conditions, thereby enhancing overall mental wellness.
Amruth and his company, Addera, provide a strong example of how to collect new kinds of data and integrate it into a product to provide better access and quality of care: "We are building Addy - an AI coach for mental health, starting with coaching for adult ADHD. Addy uses AI models to construct, test and fine-tune personalized protocols that can take every person through a unique journey towards better mental health in a way that's aligned with their individual strengths and difficulties."
To further split up the field we can functionally divide applications according to the different problems of mental health tech, they are solving:
Gathering of Data
Traditionally assessment of conditions is often carried out through subjective means such as surveys. The assessment is regularly self applied and left up to the patient. Gathering of data is usually infrequent and limited to a clinical setting.
AI solution: AI enhances assessment accuracy through continuous, objective data collection integrated into daily routines, supplemented by insights from wearables and other tech sources.
Examples: Sonde: Vocal biomarker technology that can tell you are at risk for conditions of mental health. Series B $41.3M (Backed by Partners and M Ventures)Kintsugi: Detects signs of clinical depression and anxiety using machine learning and voice biomarkers. Series A $30.9M (Backed by techstars_ and Insight Partners)
Utilization of Data
Due to subjectivity of measurements it can be difficult to quantify progress. Moreover it is difficult to attribute progress to any particular reason.
AI solution: We can gather much larger data sets. This requires us to use AI to provide insights. We can get insights that were previously not possible and personalize solutions for every user.
Examples: limbic: AI-based tool that supports assessments and referrals in psychological therapies. Series A (Backed by 7 Percent Ventures). Unmind: B2B mental health platform providing clinically-backed tools and training. Series B $63.2M (Backed by EQT and Project A).
Communication of Insights
Interactions between patients and clinicians are infrequent and delivered only in scheduled appointments. Recommendations are often difficult to implement in daily life.
AI solution: LLMs can make communication with users easier. Solutions can be implemented into other aspects of people’s lives, bringing treatment to them directly.
Examples: Woebot Health: Automated conversation agent using techniques from CBT to help monitor mood and enables users to learn about themselves. Series ? $123.3M (Backed by TEMASEK and NEA).Wysa: Mental health wellness platform that helps individuals manage their mental and emotional stress. Series B $29.5M (Backed by Google Assistant and Kae Capital).
AI's role in mental health care is evolving from a supportive tool to a potential catalyst for a comprehensive understanding of the mind and its maladies. The collective efforts to implement AI responsibly and creatively can revolutionize our approach to mental health care, making it more accessible, personalized, and effective. This revolution is not just within our reach—it is unfolding before us, and it is imperative that we nurture its growth with every resource at our disposal.
Thanks for reading, and please let me know what you think, what further opportunities in the market you see or what technological trends on the horizon you believe will make an impact. A special thanks goes out to Eduard Hübner, who was my great co-author for this piece.
- Rasmus
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