Thank you very much, thank you for the introduction. It's going to be a bit of a change in shifts in terms of the scale of the project I'm working on.
That's the title of my project and I should say it's been funded by my senior fellowship, to my own debates and their fellowship.
And it's a project that is a bit more theoretical in some ways.
I again, to the one that that is, and although there is a strong theoretical component, and I think there is also overlap,
as you would say, between some particular kind of principles, health principles and notions that are used in my project as well.
So I would like to start with just giving a definition of what I mean by a I just to make sure that we're all on the same page.
So I think I mean, computational systems used to resolve problems that would normally require human intelligent input to be resolved.
And this is a technology that has been heralded as kind of bringing about a new, exciting revolution in health care.
So A.I. has a great potential to improve health outcomes, as we've heard, as well as the individual,
but also the population level assist cash-strapped health care systems like the
NHS at the moment by significantly increasing efficiency and effectiveness
in the way that we use resources and generally improve access to health care and health care services for the population and individuals as well.
So some of the ways in which I can have a positive impact, particularly on population health,
are through, for example, more effective prevention methods,
providing more accurate early diagnosis of conditions, better monitoring of infectious diseases and assisting with epidemic prediction.
We're going through the coronavirus epidemic at the moment, so air is one of these things that could definitely help kerb the spread of the disease.
Also through personalised promotion of healthier living.
So the thing about apps that they can give you advice on what to eat and how
much to exercise and not exercise based on your particular health risk profile.
Also, air tools can be used in self-management of chronic diseases like diabetes or certain types of cardiovascular diseases,
and also improve the efficiency of patient management on the ground for us to assess health care systems.
We very often hear in the news how the waiting time in A&E are getting longer and longer,
and patients cancer patients cannot access care as fast as they should,
and there are already items developed and implemented in hospitals around the country to assist in dealing with these type of problems.
And of course, it's not the first time that the health care and health systems are turning to technologies
to kind of help them deal with and improve health outcomes and deal with problems as well.
I mean, think of the stethoscope.
It was one of these kind of first technology documents the hearing of healthcare professionals that could provide much more accurate diagnosis,
that they could hear what was going on. Same with X-rays augmented our capacity to see inside the body without having to open the body up.
So it's as I said, it's not the first time that we have technologies coming in.
But I believe that air is different because the aim of AI is not to augment our physical capabilities,
but in a way it's trying to augment our rational decision making capacity and lead to rational action.
And it is this understanding of rationality, I think,
as being the ability to compute solutions that would optimise certain values given
the available information that we have is what makes a different type of technology.
I think that Stuart Russell called that calculative rationality, and I think it's one that can be truly disruptive in this area of health care.
And what I mean by disruptive.
What I mean is that it can fundamentally change our expectations and also our perception of what health care is about, what is trying to achieve.
Also the role of healthcare professionals and the role of systems within that context.
And with this in mind, I would like to turn to these three notions of I'm kind of particularly interesting theme of efficiency, accuracy and trust.
So as we try to draw from cloud as well and make before efficiency and accuracy are obviously clearly important for patients,
for health care professionals and for the health care system as a whole.
Having access to more accurate diagnoses or more accurate information about the spread of infectious
diseases means a much increased probability to have a positive outcome and effectively save lives.
Equally, greater efficiency and effectiveness in the way that we use our resources.
Time, like the time of health care professionals financial resources means waste avoidance and my ability to
channel effort to the right place in the right way at the right time and again improve health outcomes.
But what so what we're kind of theoretically facing in front of us and maybe, you know,
maybe it is already kind of happening out there as some of the examples are coming already
through is a kind of win win situation where patients benefit from more accurate diagnoses,
targeted screening prevention, better outcomes and health systems, increase efficiency, reduce costs and keep populations have that for longer.
However, it is currently, I think,
not quite certain whether the incorporation of A.I. into population health and healthcare systems in general will be as frictionless and
as unproblematic as we would like to hope that it will be and how to address potential ethical issues that might arise in the context.
And I think one of the issues that is particularly interesting and important that we need to address in this context is the issue of trust.
So the way I understand trust is like trust is a fundamental value of health care
provision and underpins the relationship between patients and the health care system.
Patients trust health care professionals and the system as a whole because they believe that people working in it have the right skills,
the right knowledge, the right expertise and also more latitude to care for them.
So we often hear how the NHS is the most trusted institution in the UK.
So what characterises trust is an acceptance of and of uncertainty from the position of
the patient and the position of vulnerability that the patients put themselves into,
which is, however, justified because we have this belief that the people who are caring for us have the right skills,
but also the right attitude towards us that they are, they have a good disposition towards us.
So the question then becomes like if we start outsourcing clinical expertise or the skin side of trust to machines,
would that challenge this trust and alter perceptions regarding health care professionals and their role within the system?
So it is a question of whether patients,
once they consider doctors and nurses as trustworthy professionals whose recommendations they ought to follow when when they went to see the.
But trust is also important in the relationship that health care professionals develop with technologies and that technology providers as well.
Companies developing a I already know that the consumer trust is crucial for the acceptability of that product.
It is particularly important in care provision where the health care system has a fiduciary obligation for patients to always act that their best
interests are what the doctors and nurses be confident to prescribe a self-monitoring choice to patients if they do not understand how they work.
How should this agreement between health care professionals and A.I. systems be resolved?
Should doctors override air produce recommendations or not? I think probably you are familiar.
We are back with a IBM Watson Watson case.
I think 2018 that developed a cancer diagnosis tool that was introduced in many hospitals to some hospitals in the US and within,
I think within the year or less than that, it was taken down.
And the main reason was that the healthcare professionals do not trust the tool, so they wouldn't use it.
However, I mean, it is true that as A.I. develops and improves, I mean, the hope,
at least is that and as the accuracy of prediction above gets better and better,
and these tools improve the accuracy that these tools will kind of replace the need for for trust.
Some some theory is kind of suggesting that. So the hope is that their risk profile, introduction and diagnosis becomes more accurate.
With the use of A.I. in big data, healthcare professional patients and the public will not need to trust anymore by
the will and instead to rely on intelligent machines to guide health behaviour.
So the likely effects of a health care system that is reliable but not necessarily trusted or trustworthy are unclear.
Could it improve health outcomes and save more lives? Or that lead to the reinforcement of more reductionist view of health care,
which has been so far associated with poor health outcomes and patient outcomes?
And beyond this kind of practical concerns is something intangible yet more important lost if trust becomes obsolete in the health care system.
So these are some of the questions that my project is trying to.
I'm trying to answer. So I would like to explore.
I'm starting to explore this relationship between efficiency, accuracy and trust in an AI augmented population, health and health care.
So questions like what is the value and meaning of these notions in this new in this context?
Should we start shifting our understanding of what is required in health?
Or should we try to steer technology to accept some and incorporate certain established values into their system as well?
And I think these are both practical questions, but also importantly,
ethical questions that we will need to resolve as we move forward with with AI in healthcare.
Thank you very much.