Can Artificial Intelligence Replace Human Therapists?

Could artificial intelligence reduce the need for human therapists?

Websites, smartphone apps and social-media sites are dispensing mental-health advice, often using artificial intelligence. Meanwhile, clinicians and researchers are looking to AI to help define mental illness more objectively, identify high-risk people and ensure quality of care.

Some experts believe AI can make treatment more accessible and affordable. There has long been a severe shortage of mental-health professionals, and since the Covid pandemic, the need for support is greater than ever. For instance, users can have conversations with AI-powered chatbots, allowing then to get help anytime, anywhere, often for less money than traditional therapy.

The algorithms underpinning these endeavours learn by combing through large amounts of data generated from social-media posts, smartphone data, electronic health records, therapy-session transcripts, brain scans and other sources to identify patterns that are difficult for humans to discern.

Despite the promise, there are some big concerns. The efficacy of some products is questionable, a problem only made worse by the fact that private companies don’t always share information about how their AI works. Problems about accuracy raise concerns about amplifying bad advice to people who may be vulnerable or incapable of critical thinking, as well as fears of perpetuating racial or cultural biases. Concerns also persist about private information being shared in unexpected ways or with unintended parties.

The Wall Street Journal hosted a conversation via email and Google Doc about these issues with John Torous, director of the digital-psychiatry division at Beth Israel Deaconess Medical Center and assistant professor at Harvard Medical School; Adam Miner, an instructor at the Stanford School of Medicine; and Zac Imel, professor and director of clinical training at the University of Utah and co-founder of LYSSN.io, a company using AI to evaluate psychotherapy. Here’s an edited transcript of the discussion.

Leaps forward

WSJ: What is the most exciting way AI and machine learning are being used to diagnose mental disorders and improve treatments?

DR. MINER: AI can speed up access to appropriate services, like crisis response. The current Covid pandemic is a strong example where we see both the potential for AI to help facilitate access and triage, while also bringing up privacy and misinformation risks. This challenge—deciding which interventions and information to champion—is an issue in both pandemics and in mental health care, where we have many different treatments for many different problems.

DR. IMEL: In the near term, I am most excited about using AI to augment or guide therapists, such as giving feedback after the session or even providing tools to support self-reflection. Passive phone-sensing apps [that run in the background on users’ phones and attempt to monitor users’ moods] could be exciting if they predict later changes in depression and suggest interventions to do something early. Also, research on remote sensing in addiction, using tools to detect when a person might be at risk of relapse and suggesting an intervention or coping skills, is exciting.

DR. TOROUS: On a research front, AI can help us unlock some of the complexities of the brain and work toward understanding these illnesses better, which can help us offer new, effective treatment. We can generate a vast amount of data about the brain from genetics, neuroimaging, cognitive assessments and now even smartphone signals. We can utilize AI to find patterns that may help us unlock why people develop mental illness, who responds best to certain treatments and who may need help immediately. Using new data combined with AI will likely help us unlock the potential of creating new personalized and even preventive treatments.

WSJ: Do you think automated programs that use AI-driven chatbots are an alternative to therapy?

DR. TOROUS: In a recent paper I co-authored, we looked at the more recent chatbot literature to see what the evidence says about what they really do. Overall, it was clear that while the idea is exciting, we are not yet seeing evidence matching marketing claims. Many of the studies have problems. They are small. They are difficult to generalize to patients with mental illness. They look at feasibility outcomes instead of clinical-improvement endpoints. And many studies do not feature a control group to compare results.

DR. MINER: I don’t think it is an “us vs. them, human vs. AI” situation with chatbots. The important backdrop is that we, as a community, understand we have real access issues and some people might not be ready or able to get help from a human. If chatbots prove safe and effective, we could see a world where patients access treatment and decide if and when they want another person involved. Clinicians would be able to spend time where they are most useful and wanted.

WSJ: Are there cases where AI is more accurate or better than human psychologists, therapists or psychiatrists?

DR. IMEL: Right now, it’s pretty hard to imagine replacing human therapists. Conversational AI is not good at things we take for granted in human conversation, like remembering what was said 10 minutes ago or last week and responding appropriately.

DR. MINER: This is certainly where there is both excitement and frustration. I can’t remember what I had for lunch three days ago, and an AI system can recall all of Wikipedia in seconds. For raw processing power and memory, it isn’t even a contest between humans and AI systems. However, Dr. Imel’s point is crucial around conversations: Things humans do without effort in conversation are currently beyond the most powerful AI system.

An AI system that is always available and can hold thousands of simple conversations at the same time may create better access, but the quality of the conversations may suffer. This is why companies and researchers are looking at AI-human collaboration as a reasonable next step.

DR. IMEL: For example, studies show AI can help “rewrite” text statements to be more empathic. AI isn’t writing the statement, but trained to help a potential listener possibly tweak it.

WSJ: As the technology improves, do you see chatbots or smartphone apps siphoning off any patients who might otherwise seek help from therapists?

DR. TOROUS: As more people use apps as an introduction to care, it will likely increase awareness and interest of mental health and the demand for in-person care. I have not met a single therapist or psychiatrist who is worried about losing business to apps; rather, app companies are trying to hire more therapists and psychiatrists to meet the rising need for clinicians supporting apps.

DR. IMEL: Mental-health treatment has a lot in common with teaching. Yes, there are things technology can do in order to standardise skill building and increase access, but as parents have learned in the last year, there is no replacing what a teacher does. Humans are imperfect, we get tired and are inconsistent, but we are pretty good at connecting with other humans. The future of technology in mental health is not about replacing humans, it’s about supporting them.

WSJ: What about schools or companies using apps in situations when they might otherwise hire human therapists?

DR. MINER: One challenge we are facing is that the deployment of apps in schools and at work often lacks the rigorous evaluation we expect in other types of medical interventions. Because apps can be developed and deployed so quickly, and their content can change rapidly, prior approaches to quality assessment, such as multiyear randomized trials, are not feasible if we are to keep up with the volume and speed of app development.

Judgment calls

WSJ: Can AI be used for diagnoses and interventions?

DR. IMEL: I might be a bit of a downer here—building AI to replace current diagnostic practices in mental health is challenging. Determining if someone meets criteria for major depression right now is nothing like finding a tumour in a CT scan—something that is expensive, labour-intensive and prone to errors of attention, and where AI is already proving helpful. Depression is measured very well with a nine-question survey.

DR. MINER: I agree that diagnosis and treatment are so nuanced that AI has a long way to go before taking over those tasks from a human.

Through sensors, AI can measure symptoms, like sleep disturbances, pressured speech or other changes in behaviour. However, it is unclear if these measurements fully capture the nuance, judgment and context of human decision making. An AI system may capture a person’s voice and movement, which is likely related to a diagnosis like major depressive disorder. But without more context and judgment, crucial information can be left out. This is especially important when there are cultural differences that could account for diagnosis-relevant behaviour.

Ensuring new technologies are designed with awareness of cultural differences in normative language or behaviour is crucial to engender trust in groups who have been marginalised based on race, age, or other identities.

WSJ: Is privacy also a concern?

DR. MINER: We’ve developed laws over the years to protect mental-health conversations between humans. As apps or other services start asking to be a part of these conversations, users should be able to expect transparency about how their personal experiences will be used and shared.

DR. TOROUS: In prior research, our team identified smartphone apps [used for depression and smoking cessation that] shared data with commercial entities. This is a red flag that the industry needs to pause and change course. Without trust, it is not possible to offer effective mental health care.

DR. MINER: We undervalue and poorly design for trust in AI for healthcare, especially mental health. Medicine has designed processes and policies to engender trust, and AI systems are likely following different rules. The first step is to clarify what is important to patients and clinicians in terms of how information is captured and shared for sensitive disclosures.

AI agents like Muse can already find you the cheapest hotel room in Paris. But they can’t locate a cheaper version of that Catskills cabin you’ve been eyeing, because it might only be available on Airbnb ABNB -1.43%decrease.

Investors should pay attention to that distinction. When Meta Platforms META 3.38%increase launched its Muse agent last month, travel stocks including Booking Holdings BKNG -1.19%decrease, Expedia and Airbnb all fell sharply.

Airbnb, though, has a real chance to emerge as a winner from the upheaval. The catch: Its stock already reflects above-industry growth. To justify its valuation, Airbnb will need to turn repeat customers into loyal ones.

Online travel companies make much of their money by simplifying search and collecting fees on bookings. AI agents threaten that model.

But not all travel marketplaces are equally exposed. Booking and Expedia largely sell hotel rooms that travelers can also book directly or through competing sites. If an agent can find the same room elsewhere, it has little reason to favor either Booking or Expedia.

Booking and Expedia also sell sponsored listings that steer travelers toward particular properties. Wells Fargo estimates those will account for about 8% of Expedia’s revenue and a much higher percentage of profit next year.

Airbnb has more ways to adapt. More than half its listings are available only on its platform, and more than 90% of its traffic comes directly to its app or website.

The customer relationship doesn’t end after booking, either. Bernstein analyst Richard Clarke notes that 90% of Airbnb guests go on to message their hosts about arrival times, keys or local taxes, giving Airbnb a connection to both sides of the transaction.

How the companies have responded to the possible agentic future is telling.

Expedia quickly signed on as a Muse partner. Airbnb hasn’t, and Chief Executive Brian Chesky says AI chatbots have so far been a boon, sending customers to Airbnb’s website.

Chatbots might work for buying paper towels, he quipped at a Skift conference last month, but people enjoy planning trips. He argued that browsing dozens of homes isn’t something a text-based chatbot is well suited for.

Still, the AI jitters arrived just as Airbnb was persuading investors it deserved its pricey multiple. For years after its 2020 initial public offering, the stock—which trades around 25 to 30 times forward earnings—went nowhere. Investors saw a mature operator trading at a growth premium.

This year, Airbnb growth seems to be accelerating. The stock jumped in August after the company raised its full-year revenue-growth guidance with its second-quarter results. Wells Fargo analyst Ken Gawrelski credits a run of product improvements under a revamped leadership team.

Among the changes: letting guests reserve now and pay later, adding hotels, and a redesigned home page and app. Investors were also betting on what comes next, including thousands more hotels, car rentals and, eventually, advertising.

The irony is that some of those growth drivers push Airbnb closer to the kind of business Booking and Expedia run—the kind of business AI agents could threaten. Even if AI agents capture a large share of travel planning and booking, Airbnb would capture the demand. But it and others would slowly lose control over how customers find and choose properties.

That would undermine the advertising opportunity investors are counting on. The bigger point for Airbnb and other marketplaces is that losing control of customer acquisition could mean surrendering some of their pricing power and margins.

To bulletproof itself, Airbnb might look to another e-commerce giant that has so far kept AI agents at arm’s length: Amazon.

Like Amazon, Airbnb has a loyal customer base and hard-to-match supply. But it has nothing like Prime membership to make customers think twice before taking their business elsewhere.

That is where loyalty comes in. Business travelers stick with Hilton or Marriott because enough nights earn them perks. Leisure travelers rarely accumulate stays with one chain. Airbnb has millions of listings and plenty of repeat customers, but no broad rewards program. It has begun experimenting with credits on hotel stays, but it should make loyalty a priority, not just a promotion.

Airbnb also plans to launch its own AI agent next year. But travelers using an agent might still want it to search Airbnb, Vrbo, Booking and hotel websites alike, rather than limit itself to one company’s inventory.

Eventually, Airbnb might have to strike deals with agent makers, using its unique homes as leverage to negotiate favorable terms, says Gawrelski. Such deals, along with rewards for loyal guests, could over the long term eat into the company’s roughly 13% take rate, he adds. But if they bring in enough additional bookings, the trade-off could be worthwhile.

History offers a reason not to panic. The shift from desktop to mobile unsettled investors in Alphabet and Amazon, but both emerged as major beneficiaries. Companies with valuable assets can come out of a platform shift stronger, if they adapt quickly enough.

Airbnb has homes the bots can’t easily replace. Now it needs to give guests a reason to insist on booking them there.

Can Artificial Intelligence Replace Human Therapists?

Three experts discuss the promise—and problems—of relying on algorithms for our mental health.

Leaps forward

Judgment calls

AI-powered travel agents are challenging online booking platforms, but Airbnb’s exclusive listings and direct customer relationships could give it an edge. Its next challenge: turning repeat guests into loyal customers through stronger rewards and innovation.