AI and Mental Health: Challenges and Opportunities
AI has already shown promise in various healthcare applications, from diagnostics and personalized health interventions to chatbots and virtual assistants for patient interactions.
But can AI also be an effective tool for treating mental health conditions? What are the risks? And do AI-driven tools represent a possible improvement over current mental health treatments?
We dive into all this, and more, below.
The Current State of Mental Health
While the medical profession has certainly come a long way in the treatment of mental health conditions, the numbers paint a disturbing picture of a growing mental health crisis in the U.S. and elsewhere:
- Nearly one-quarter of U.S. adults experienced a mental health condition in 2022; six percent of adults experienced serious mental illness
- More than 30 percent of U.S. adults experienced both mental illness and substance abuse in 2022
- Young adults (ages 18-25) have the highest rate of mental illness in the U.S. at more than 30 percent, and the highest rate of serious mental illness at more than 10 percent
- Depression and anxiety have a $1 trillion impact, in terms of lost productivity, on the global economy each year
The trend towards deinstitutionalization in the U.S. beginning in the 1950s and 60s has, many argue, only made this crisis worse by lowering the number of available psychiatric beds.
How Can AI Improve Mental Health Care?
It’s clear that current approaches of treating mental illness aren’t enough to rectify the mental health crisis – or even stop it from getting worse.
More generally, healthcare staffing problems that cropped up at the start of the pandemic haven’t gone away and continue to impact health outcomes and facilities for both psychiatric and non-psychiatric patients alike.
AI has the potential to significantly alleviate these issues and contribute to mental and behavioral health in various ways.
One of the most obvious is AI’s ability to save on administration costs through more efficient medical record keeping, prescription management, and insurance billing. AI has also been shown to improve patient engagement and can help scale the effectiveness of human physicians by automating menial or repetitive tasks.
But two of the most impactful ways AI can assist clinicians and health systems when it comes to mental health are in detecting, diagnosing, and managing mental illness through personalized treatment.
Early Detection and Diagnosis: AI algorithms can analyze patterns in large datasets, including social media posts, medical records, speech patterns, and physiological data, to identify potential signs of mental health issues at an early stage. These datasets are often unavailable to human therapists.
Natural language processing (NLP) can analyze text and speech, helping to detect sentiment and emotional cues indicating mental health concerns. It has even been claimed that AI algorithms can detect depression by analyzing a person’s smartphone typing patterns.
AI has been used in three main ways in this regard, according to Minerva et al.:
- Digital phenotyping: Also known as “personal sensing”, this uses digital data to monitor and measure a person’s mental health by detecting subtle behavior changes (such as a smartwatch detecting an uncharacteristic lack of exercise, for example).
- NLP analysis: Tracking the language used in digital conversations can determine whether a patient shows signs of mental issues, or whether those issues are improving (or worsening).
- Chatbots: Studies say medical chatbots can detect mental illness by asking questions and analyzing a patient’s answers, the same as a human physician would.
Personalized Treatment Plans: AI can analyze individual patient data to create personalized treatment plans and medical interventions based on genetic predispositions, lifestyle, and response to previous treatments. Machine learning (ML) algorithms can continuously adapt treatment plans based on patient feedback and progress (patient data is generated and automatically sent to an ML system by wearable devices).
AI can also be an effective vehicle for delivering specific mental health treatments, such as cognitive behavioral therapy (CBT).
A new computational model of the human brain developed at the Université de Montréal, for example, could improve personalized psychiatric treatments by allowing clinicians to rely on objective biomarkers to guide decision-making (rather than on a case-by-case basis).
And thanks to the social stigma of mental illness, another potential benefit of AI-driven mental health treatment is that more people may be willing to come forward to talk about their mental illness if they don’t have to share personal details with a human.
What Are the Risks Involved in AI for Mental Healthcare?
Some of the potential risks associated with AI-driven mental health treatments include the dehumanization of healthcare, largely because the human element of a therapist-patient relationship is much greater than in other healthcare scenarios. Such dehumanization could lead to a loss of empathy and trust.
Other potential downsides of AI for mental healthcare could include:
- False positives, especially in cases of digital phenotyping (someone who suddenly stops exercising may not be depressed and could be dealing with other health issues, or caring for a family member).
- Potential mistakes made by “therapy bots” performing psychotherapy or similar treatments with mental health patients who are depressed and highly vulnerable. “Psychotherapists are professionals with licenses and they know if they take advantage of another person’s vulnerability, they can lose their license. They can lose their profession,” explains UC Berkeley School of Public Health Professor Dr. Jodi Halpern. “AI cannot be regulated the same way.”
- Dr. Halpern adds that there’s also a risk of patients becoming “addicted to using bots” and withdrawing from engagement with humans, along with a risk of “bots going rogue and advising people to harm themselves.”
Other potential issues around AI in mental healthcare include ethical considerations, privacy, and data security.
Overcoming Potential Problems With AI
But there are plenty of ways of overcoming AI’s limitations, and they start during the training phase: In most cases, AI models need to be trained on significant amounts of high-quality, unbiased data in order to produce the most relevant, unbiased, and accurate outputs.
Additionally, AI outputs should always be used as a recommendation and not a replacement for human judgment. AI models for healthcare also aren’t “set and forget” tools: When used to treat mental health conditions, they must always be overseen by a therapist or mental health practitioner.
Other ways mental healthcare organizations can ensure the responsible, effective use of AI include establishing rules around data privacy and consent. Data must always be kept secure to guard against potential data breaches, and users must know how their data will be stored, accessed, and used.
Finally, there needs to be a chain of accountability when AI tools are deployed in case of negative healthcare outcomes (which, in fairness, can and do occur in situations where AI is not involved). AI developers, therapists, practice owners, and other stakeholders must have these conversations in advance of deploying AI tools.
Conclusion
AI holds plenty of promise around mental illness detection, diagnostics, and personalized treatment. AI can improve mental healthcare efficacy and efficiency while lowering overall costs. But to build trust among physicians and patients, the industry must also address the risks of AI in mental healthcare.
CapeStart works with healthcare organizations, researchers, and clinicians to develop AI and ML systems for pharmacovigilance, systematic literature reviews, clinical evaluation reports, medical image annotation, and other healthcare applications. Contact us today to learn more.