- I think your average person in the street
thinks we all share information appropriately
and wonderfully in health care.
Electronically, seamlessly, routinely. We don't.
We have many more problems from an inability
to share information than we do
from inappropriate sharing of information.
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- There is a fair group of people
who think that doctors will become extinct.
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I am not in that group.
I think that the role of doctors will change.
- You can't ever replace this conversation that you'll have,
with a person, with technology.
That human connection in the space of wellbeing,
and with young people is incredibly important.
- The doctor can be subject to fatigue, tiredness,
and he or she can make mistakes.
The main objective that we really want to achieve
is to help doctors be more consistent between each other
and to improve their accuracy.
- We've had a whole debate around My Health Record,
and we had 2 million people who opted out.
Why are they expressing concerns?
Because they weren't consulted
and a lot of people are suspicious about
the harvesting of their data.
- We are basically these days in a sort of a war.
An unofficial war when it comes to electronic information.
There's a whole bunch of bad people trying to access it,
and there's a whole bunch of good people paid to
and constantly exploring ways to keep them from it.
- Probably one of the challenges is there's not
a universal description and understanding
of what digital health means.
We don't have a frame work necessarily,
that's come from United Nations
or The World Health Organization,
that clearly defines all the parameters of the space.
- If people can't agree on the terms
as used in different contexts in different ways,
then how are we going to develop effective policies
and how are you going to agree on what's
the best way forward to protect the public's interests.
- I took quite a while to come to a very simple,
one line definition of it.
I've defined it here as the use of digital tools
and interventions in wellness and healthcare.
- The way we're solving seizure prediction,
takes advantage of all different ways
of doing digital health.
So not just basically developing machine learning
or A.I. algorithms but taking advantage of cloud computing
to make data available for other researchers
around the world.
We don't really know what algorithm's best
and so by linking everything together
all these digital tools, we're able to
work towards solving a really hard problem in epilepsy,
which is seizure prediction.
- What I'm doing with my team,
mainly developing computer programs which can be integrated
with the digital images to help doctors
to detect diseases more accurately.
- Then in Melbourne we ran a clinical trial
of a seizure prediction device that had the device implanted
for up to three years in duration.
And so, with that device we were able to get long term data,
and therefore be able to get enough seizures
from each patient to be able
to develop reliable algorithms.
The predictions we generate could be used to activate
a brain implant that could electrically stimulate the brain
or deliver drugs to stop seizures from happening.
We're also working with Melbourne Uni and Seer Medical
and supported by Epilepsy Foundation of America
and The Mayo Clinic.
- Studies frequently show that they cannot agree
amongst each other.
For example, whether there's lung cancer or not.
Which is actually very scary from the patients perspective.
- We are planning to develop an app
which will identify from a photograph taken from
a handheld device the nutritional content
of a plate of food.
- Monash Malaysia, we have some new courses introduced
for artificial intelligence.
Right now, in the unit that I'm teaching, Computer Vision,
there's more emphasis and more material being introduced,
in deep learning, due to deep learning becoming
such a popular subject.
- The use would be extensive, not just for
diabetes or hypertension, or obesity,
but for normal people like you and I,
because it's such a common thing for people
to take photographs of their food.
- We gave them a little device, what we call
a telemonitoring device, where,
what they would do is they would measure their sugar levels.
And what we could do is we could actually analyze it,
and give
basic advice on
diet,
basic advice on medications,
whether we need to adjust medications to patients.
We could actually send them advice that said,
"Look, your sugar levels are just too low. You need
to do something.
So perhaps, you could see a doctor,
you should be adjusting your medications."
We became sort of a middle person,
between the doctor and the patient.
We could be a game changer.
- So Ash is a robot, but it's powered
by artificial intelligence and machine learning.
Ash just basically gives educational information
about mental health and wellbeing.
So conversation might be a young person says,
"I'm really struggling today,
I feel like I'm losing friends."
And then Ash will respond with supportive comments,
like, "Tell me more. What's happening?"
And if they're at risk of hurting themselves,
there's an alert trigger in Ash,
so, you know, a caring person, or a teacher,
or the wellbeing support team are alerted,
and they can step in and help that young person.
- I see the internet as a dangerous space.
It's one where there's a lot of optimism,
but it's also one where there's a lot of dangers,
and where people can be mislead, deceived, manipulated
in various ways.
- Careful embedding of the technology
in the human-to-human contact,
so that,
we are not
actually causing harm
is really an important aspect of the whole situation.
These are very vulnerable populations
that we are working with.
And great damage could be done,
if we don't deal with them in a sensitive fashion.
If, imagine, in a very heavy handed fashion
we come in and say, "Okay, we're going to do away
with all of this, and now on, A.I. programs
are going to teach homeless youth about risks of H.I.V.
From now on, people who are going to remind T.B. patients
to take their medicine, and all of that,
that's all going to be this A.I. program
that's going to do all of that."
You can imagine that this can actually cause
tremendous harm to the communities.
- You don't know what it's like waking up every morning,
and the first thing you look at is Instagram,
and super models doing all sorts of things,
and then you turn to Tumblr to get some help,
to cope,
and it's telling you to self-harm.
So something like a chatbot in a school setting,
that could give them that information automatically,
where they don't need to turn to the internet
to get that support, will hopefully be quite effective.
- We have to always remember that at the end of the day,
successful healthcare, especially, is about helping people.
And there's not question about the value
of the personal relationship in that.
Still hear a lot of people talking and writing like,
the machines are completely autonomous,
and we're almost pre-Terminator,
and they're just going to march in and say,
"You do not have a job anymore."
- What has happened is that now it seems like,
computers with good algorithms,
can actually interpret these things better
than a human being can.
And over time they get better and better,
because they learn.
They keep learning.
And as they learn, that learning remains within the system.
And then over a period of time,
it becomes extremely powerful.
- We are
not trying to eliminate the human in the loop.
When we are talking about H.I.V. prevention,
and educating youth about H.I.V. risks, and so forth,
it is a social worker who's talking to the youth.
On the other hand, where the A.I. technology is useful,
is in identifying which peer leaders to select
in the first place.
So there's places where the technology
is actually beneficial,
and we should identify those places.
- A lot of it's basic public health.
We've got a large portion of the population today,
at the global level, and even in Australia,
who live in poverty, or don't have access
to clean water or good shelter.
Yet we keep pouring money into the technological fix.
Whereas low-tech, or no-tech solutions
are the ones we urgently need.
- The machine makes a mistake,
then who's going to be blamed?
Will the engineer who developed the machine be blamed?
Or will the company who developed the machine be blamed?
Or will the doctor who is in charge be blamed for that?
- In those individuals that we actually spoke to,
so a lot were really optimistic about digital health.
They really see it as the future of healthcare.
However, some did raise some concerns
in terms of data privacy.
Just the wearables that we are using,
a lot of data gets uploaded, online,
but really- they were wondering what really happens
to this data?
- What do we do with that data,
and does it end up getting held by the school?
And what we're thinking is, the wellbeing team,
will get access to that,
and it will be following the same sort of confidentiality
as they would if they were having a session with
a school counsellor.
It's a big challenge.
- The biggest companies in the world,
they're in the business of monetising personal data.
I think people have got a lot to be concerned about,
if they're handing over their information,
their health information, which is very private information.
- Health data is much, at a higher risk.
If the risk of my illness is known, you know,
I have 50% chance of getting a heart attack
in the next five years.
If that data were to go to corporate companies, you know,
let's say, insurance companies.
They can use that against you.
They can use that to say, "We're not going to insure you."
- If you have all of Australia putting in their data,
and sharing it,
and you derive conclusions from all of that data,
then you know, you can make conclusions that
are actually useful.
That's the benefit of doing it.
So for me, the benefits outweigh the security risks.
- This is coming so quick and so fast,
I think nobody has got around to understanding it,
and how to manage it, how to regulate it.
- I think there's a bit of an A.I. gold rush on
at the minute.
And I think a lot of people are spending a lot of time
and effort in that space,
running off in lots of directions,
because they think there's a lot of money to be made.
- That A.I. will be incredibly important,
in the future, as we move forward
in the digital health space.
I don't think we should be afraid of A.I.
and what it can produce.
- When have we succeeded in digital health,
is when we stop using the word digital.
Where now the healthcare we provide,
and the wellness care we provide is just inherently using
I.T. and tools like it to the right safe extent.