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Printable Handouts
Navigable Slide Index
- Introduction
- The challenge of scale
- The future in medical knowledge
- Professional skills in routine practice
- Size and scope of medical practice
- Modelling practice - clinical guidelines
- Options for scaling up (1)
- Options for scaling up (2)
- Crowdsourcing executable models of practice
- Example: prevention of secondary strokes
- Example: cancer
- Options for scaling up (3)
- Machine learning
- “Classifiers” in medicine
- Some general learning methods
- Learning from data
- What’s coming?
- Knowledge engineering integrated with data science
- Learning: climbing the knowledge ladder (1)
- Learning: climbing the knowledge ladder (2)
- The knowledge-data “ecosystem”
- Recap
- Data science meets knowledge engineering
- Further reading
- Thanks you
Topics Covered
- The Challenge of scale
- Crowdsourcing executable models of practice (publets)
- Machine learning
- The future of artificial intelligence in medicine
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Talk Citation
Fox, J. (2019, October 31). Artificial intelligence in medicine: scaling & deployment of applications [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved December 7, 2024, from https://doi.org/10.69645/HAGQ7673.Export Citation (RIS)
Publication History
Financial Disclosures
- Commercial/Financial matters disclosed are Prof Fox is a founder and shareholder of Deontics Ltd (www.deontics.com)
Artificial intelligence in medicine: scaling & deployment of applications
Published on October 31, 2019
27 min
Other Talks in the Series: Periodic Reports: Advances in Clinical Interventions and Research Platforms
Transcript
Please wait while the transcript is being prepared...
0:00
This is the second part of the lecture on artificial intelligence in medicine,
in which we're going to focus on the scaling of
applications to cover a large part of medicine and its deployment.
0:14
So, now having seen that we have the capability to model
many different aspects of human expertise and knowledge in a practical way,
it can be used to support clinical practice.
Now, we obviously want to work to evolve,
why can't we do this for all of medicine?
This is the issue of scalability that we referred to earlier.
0:34
Many people in the AI medical fields have
aspired to deliver services across a wide range of medicine.
A famous quote by Robert Greenes,
an American researcher in the field,
in his book, Clinical Decision Support, The Road Ahead,
painted a picture of a future in which a large proportion of
medical knowledge and practice was captured in
a computerized form and stored in a repository-
a bit like the original Oxford system of medicine dream.
But, he adds, ''if we could do that with tools for
delivering patient-specific advice at the time of need,
point-of-care, imagine what implications that would have on our ability to
deliver high-quality decision making throughout medicine. ''
That has been a vision that has driven many people in the field.
1:23
But, we have to remember thats medicine is probably one of the most complex,
if not the most complex,
and large-scale professional fields of any.
If we just look at the range of skills that a practitioner has,
it's simple things like being able to answer questions,
to recognize when there's a dangerous situation,
to reason and interpret about data.
It is also to be able to solve problems,
including some problems that perhaps haven't been seen in quite this form before;
to make decisions dealing with uncertainty,
to keep an eye on patients
to make sure that they are progressing
and detecting when something might have gone wrong.
To make sure things are done in the right order,
to plan and care in an efficient way,
and to ensure that the plans are carried out.
While at the same time on top of all of these things,
we have to keep in good communication with our colleagues and
coordinate all the different services that may be provided.
Remember, going back to breast cancer,
the people involved in looking after somebody with most cancers will be surgeons,
pathologists, radiologists, medical oncologists, clinical oncologists, and nurses.
This is a huge range of
skills that are needed for them to do their work in a coordinated way.
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