Transcriber: Gisele Cristina Ribeiro Reviewer: lisa thompson
Artificial intelligence has often been depicted as villain robots
ready to take over the world.
But I’m here to tell you that AI can actually save lives
and improve health care for millions of patients around the world.
AI is helping us personalize the delivery of care,
make hospitals more efficient,
and improve access to health care by providing accurate decision-making tools.
AI is the process of educating a computer model
using complex and large data sets.
The model learns from this data in a training process
to build its ability to make decisions
or predict outcomes when presented with new data.
We are talking about having access to a computer model that knows,
based on the experience of thousands of other patients,
whether a treatment is likely to work
and what works best for that patient based on their individual conditions.
No two of you in this room or, in fact, anywhere in the world are alike.
But AI models are helping our doctors
learn from patients with similar conditions
or even similar genetic information
and make highly informed decisions
about their diagnosis and their treatment options.
I want to talk about how we are starting to use AI
for delivering care to cancer patients.
Cancer diagnosis can be immensely complicated,
both for the doctors
in making decisions about diagnosing a primary or secondary cancer,
as well as for the patients, in understanding the risks
and success rates of the treatment options.
But we are developing AI models that can help streamline this process
by taking information from a number of sources.
This involves feeding an AI model data from the patient’s blood tests,
X-ray images of the suspected lesions,
as well as genetic information from a tissue biopsy.
The trained AI model can rapidly consolidate this information
and provide highly accurate predictions of the patient's diagnosis,
treatment options most likely to succeed, as well as the prognosis.
Let’s talk about Peter, who is a cancer patient.
He’s gone through comprehensive clinical assessment, imaging
and various other diagnostic workups,
but not even the best doctors in town can tell him where his cancer primary site is,
meaning he can’t get a treatment specific for his cancer
and his chances of surviving another five years is less than ten percent.
But our team right here in Brisbane
has developed a tool using AI and patients’ genetic information
that can accurately identify the cancer primary site of Peter,
and empower doctors
to give Peter a treatment that we know is going to work for him.
These type of models can be expanded exponentially
to predict accurate health care.
This means using an AI model
to understand whether a certain population is more susceptible to a certain disease
and whether they would respond more favorably
to certain health care interventions.
AI is giving us the ability to have a much more refined
and detailed understanding of human health than we’ve ever had before.
But there is a catch to the immense promise of AI
being implemented into routine clinical practice.
Our existing regulation frameworks aren’t designed for AI software
intended for diagnosing, treating or managing the disease,
also known as AI-based software as a medical device.
They are designed for physical medical devices, like surgical implants,
or most software that have the same output
every time that the patient or clinicians are using them.
Traditional software are static,
in a sense that the developers release a version of a software
and, no matter how many times you use it,
it would always have the same output for the same data.
On the other hand,
AI software behaves completely differently to most software in health care
because of the intrinsic ability to learn and evolve over time,
ideally becoming more intelligent
as suited to the environment that they’re being used at.
Our existing regulation frameworks
rely on the static and reproducible nature of this software
to prove that they are safe
to be implemented into routine clinical practice.
So, our regulatory authorities’ solution has been to lock the learning potential
of these algorithms before they are implemented into clinical practice.
This means that the model can no longer learn from its environment and new data,
which limits its potential to improve its functionality or its accuracy,
you know, the whole point of AI.
And, at times, this can even be harmful for the patients
because the AI model is no longer trained on the most up-to-date data
and can potentially lead to a wrong diagnosis.
But the good news is
that there are emerging regulation frameworks being proposed
that, if implemented right, can be a game changer.
Our regulatory authorities are proposing using more transparent reporting mechanism
so that the developers can disclose how their models would learn
and evolve over time.
And this will be combined with ongoing and real-time monitoring
to make sure that the predicted changes actually occur
and that the software is adaptive to make much more accurate predictions
and improve health care outcomes.
We also need to make sure
that the training data used for these algorithms
are representative of the entire human population.
Let’s look at a mobile-based diagnostic software
that we are developing right here in Brisbane
that uses AI to detect skin cancer
from the images that you’ve taken on your iPhone.
If this model has been trained on a predominantly Caucasian population,
how well do you think it would do
on an African American or an Asian patient?
Our AI developers have a huge responsibility
to make sure that data bias doesn’t exist
and that their models are trained
on diverse and robust data sets, representative of the entire population,
you know, not just white males.
But at times, we understand that this is not entirely possible.
Skin cancer does, in fact,
disproportionately affect the Caucasian population
because of the genetic differences,
and, as a result, there are much larger data sets available for those patients.
But this means that we need to build in a functionality in our AI models
that, for low confidence results, for an Asian patient, for example,
the model is capable of saying “I don’t know”
or that “This is my best guess based on a skewed training population.”
But, unfortunately, this functionality doesn’t exist yet,
and it’s urgently needed to be mandated by our regulators.
To successfully implement AI in health care,
we need to establish new regulatory frameworks
in consultation with AI developers, health care practitioners,
policy advisers,
as well as the patients themselves, to bring the best out of AI.
Improve the regulatory frameworks
can make sure that diverse and robust tools are developed
that are compliant and adaptive
and can serve the whole population equally.
If we get this right, we can transform the delivery of health care
where we are promoting personalized health and well-being advice.
I’m excited to be at the forefront of translating this amazing technology
into health care
and use this to help millions of lives around the world.
(Applause)