This is a presentation I gave at TransTech 2018 in the Bay Area - drawing from our research on mental health AI startups (https://emerj.com/ai-sector-overviews/diagnosing-and-treating-depression-with-ai-ml/). The full video of this presentation is available online: https://www.youtube.com/watch?v=CvrqoPpYF94

1. AI in Mental Health and
Wellbeing - Current
Applications and Trends
Daniel Faggella, CEO at Emerj

2. Presentation Outline
● Background in brief
● The unmet needs of mental health
● Category 1: Conversational Interfaces
○ Purported benefits / limitations
● Category 2: Behavioral Pattern Recognition
○ Purported benefits / limitations
● Believable benefits and predictions
● Risks to consider
● Concluding takeaways on this sector

3. Emerj (formerly
TechEmergence)
● Emerj is a market research firm
focused the implications of AI for
business / gvmnt leaders
● Organizations planning very
expensive technology initiatives
use us to support critical decisions
● We work with clients as large as the
World Bank, and startups who’ve
only raised $10MM

4. Emerj (formerly
TechEmergence)
● We examine AI across three
dimensions:
○ Applications (Possibilities)
○ Implications (Probabilities)
○ Strategy (Plans)
● Diagnostics, health, and mental
health have been a focus for us for
over two years, and this
presentation summarizes our
findings at the intersections of AI
and mental health

5. Note
● This presentation is remarkably short (20
minutes), so I will not be able to talk about the
full breadth of AI solutions
● My goal is to highlight the critical points
● If you’re interested in our deeper research into
individual mental health solutions, please see
the last slide in this presentation, which lists
our deeper reports and app comparisons

6. Unmet Needs of Mental
Health

7. By the Numbers
● As of 2015, of the overall NIH funding for FY 2015: $30 billion, less
than 10% went to mental health and addiction-related programs
● More than 10% of lost years of healthy life and over 30% of all years
lived with disability; WHO, 2001
● Depression is estimated to cause 200 million lost workdays each
year at a cost to US employers of $17 to $44 billion
● Also mental illness it is hard to measure and quantify (unlike, say,
pneumonia, where we can be sure when you have it, and when you
don’t)

8. Category 1:
Conversational Interfaces

9. Vendors
● Vendors: Wysa, Ginger.io, Woebot, etc
● Purported benefits:
○ Leading indicators for therapists/Drs
○ Healthy tips / reminders via chat (with
machines and humans
○ Connection to a real human coach/therapist
● Limitations:
○ Chatbots still somewhat nascent
○ Chat is only one source of data

10. Category 2:
Behavioral Pattern
Recognition

11. Vendors
● Vendors: Ginger.io, Sunrise Health (now
“Marigold”), Mindstrong, HelloJoy
● Purported benefits:
○ Leading indicators for therapists/Drs
○ On-demand connection to professionals
○ Extracting data from multiple sources
(text context in and out of app, location,
etc)
● Limitations:
○ Measuring wellbeing = hard

12. Vendors
● Data sources purportedly tracked
○ Exercise / Sleep
○ Location / movement
○ Self-report assessments (Daily, weekly)
○ Contents of text messages
○ Phone use / activity
● There are questions around both the
reliability of self report data (exercise), and
the conclusiveness of phone use data
● There are privacy concerns with this
sensitive personal data

13. Believable Benefits
and Predictions

14. ● We suspect that the core value proposition of the mental health
space lies in finding patterns across new data streams
○ Sources: Phone activity, location, app use, content of texts,
estimated hours of sleep, etc
■ Correlating new streams of real-time data (not
self-reported, but extracted from the world) to proxies for
user wellbeing (psychiatrist visits, self-reported wellbeing,
suicidal thought frequency, etc) seems promising, and AI
can be part of this mix
● We imagine a future where this proxy data can give a much more
accurate estimate of the wellbeing of a patient

15. ● We suspect that it may be years before AI can realistically suggest
actions or behavior change for the human
○ These suggestions require much more context than a chatbot
or iPhone can tell us (from childhood trauma, to fears, to other
medical issues, etc), and probably should stay in the purview
of doctors

16. ● Replicating human interaction is challenging, and seems
potentially dangerous
○ Text chat is a limited channel, and conversational AI is
exceedingly limited
■ There are a number of examples online of very disjointed or
nonsensical conversations with the bots
● Text is one potential source of feedback and data from the user,
and it could be a critical one for coaxing out high-level risks, like:
○ Asking the patient if they’re okay (if other data signals indicate
that they might be at risk)
● We feel less optimistic about chatbots encouraging behavior
change, or humans being accountable to chatbot suggestions

17. ● Conversational interfaces might be able to reach:
○ People unable to afford therapy
○ People who might be too shy or ashamed to try therapy
○ People in rural or remote areas with no therapist access
● While the opportunity to help those who cannot or choose not to
use traditional therapy, it is unclear what the best mental health
intervention is for this group of people
● It is blatantly evident that more research is needed in order to
discern the impact of different kinds of digital treatment (or no
treatment)

18. ● Good news: Startups in this sector seem to understand that the
purpose of their applications are to keep patients safe, and hand
them off to doctors
● The value proposition of Mindstrong (below) is representative of
many other firms - with “preemptive” insight being emphasized:

19. Risks to Consider

20. Business Model
● Business models create incentives:
○ Monthly fee:
■ Encourage forever use, whether or not the user needs it
○ Pay per referral to therapist:
■ Refer people to therapists, whether or not the user needs it
○ Pay per chat conversations with a therapist or “coach”:
■ Hire inexperienced / inexpensive “coaches” onto the
platform
■ Refer people to chat therapists, whether or not it’s actually
necessary or preferable

21. Business Model
● Startups have to find a way to be profitable, but it’s unclear
whether the for-profit incentives might bend the products in a
direction that doesn’t serve users
● This isn’t necessarily the fault of startups, but it is a problem that
will have to be worked out, and something that users should be
aware of

22. Long-Term Effects
● More scientific testing and trials are required here
○ We need long-term double-blind studies to assess the actual
value (and not research conducted by founders themselves)
● Testing would need to be done for any of the target groups or
target conditions
○ People in rural / remote places
○ Men / women
○ Age of patient
○ Depression / bipolar / anxiety / other

23. Conclusion and Takeaways

24. ● AI in mental health and wellness is likely to unearth solid
predictors of wellbeing and mental health risk - but we have no
robust proof at present
● It will take much longer to achieve a level whereby AI can suggest
actions and behaviors in order to tangibly improve wellbeing or
reduce risk
○ Even in 2-3 years, we don’t expect that these “suggestions”
will get beyond very generic and basic advice (i.e. “get 8 hours
of sleep” or “stay hydrated” or “do your breathing exercise”)

25. That’s All, Folks
The end.
I’ve included a list of some of the resources from this
presentation on my final “References” slide.
Feel free to send along an email with questions, or for a
copy of the slides:
dan@emerj.com
Twitter - @danfaggella
emerj.com

26. Resource List
Outside resources:
● https://www.thenationalcouncil.org/topics/federal-budget/
● http://www.who.int/mental_health/economic_aspects_of_mental_health.pdf
● https://www.wsj.com/articles/meet-the-chatbots-providing-mental-healthcare-1533828373
● https://www.himssinsights.eu/mental-health-chatbots-future-therapy
● https://woebot.io/blog/why-we-need-mental-health-chatbots/
Emerj resources:
● https://www.techemergence.com/chatbots-mental-health-therapy-comparing-5-current-ap
ps-use-cases/
● https://www.techemergence.com/diagnosing-and-treating-depression-with-ai-ml/
● https://www.techemergence.com/ai-for-mobile-medical-diagnostics-current-applications/