AI and machine learning show promise in addressing several healthcare problems by making sense of complex and diverse medical data. However, healthcare data needs to be handled carefully due to privacy and bias concerns. Radiology is one area that could benefit from AI, which has been shown to identify things in medical images well. AI may help pathologists more consistently score biomarkers like PD-L1. Pharmaceutical companies are also exploring uses of AI in areas like drug discovery and patient stratification.

1. AI and Healthcare
Paul Agapow
Oncology R&D November 2020

2. The data is
complicated &
diverse
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Labs, genomics,
clinical exams,
images, physical
measurements,
chemical, health
records, other
‘omics,
observations,
medications …
17November2020
Name

3. What are our healthcare problems?
17November2020
Name
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Gathering information
More and better data,
monitoring patients, new
molecular technologies,
imaging, devices,
integration of different
modalities, EHR records
Understanding disease
What is a disease,
pathophysiological
mechanisms, biomarkers,
patient subtypes
Developing
interventions
Finding possible targets,
candidate molecules,
running trials, analysing
trials
Delivering healthcare
Diagnosing patients,
predicting outcomes,
targeted therapy, resource
allocation & optimization

4. Messy data
But what is AI / Machine Learning / Data Science?
10
Clear
assumptions
Explicit
models
No model
Other than things we talk about a lot …
Statistical modelling Machine Learning / AI
a continuum of approaches
Few
assumptions
Clean &
controlled data
Trained from
data

5. 17November2020
Name
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• Complex multi-modal data
• Often poor idea of underlying
mechanism or model
• Messy problems with messy data
• Lots of available data (caveat)
• Many healthcare questions are classical
data questions (classify, optimize,
predict)
• Healthcare should be data-driven
• Great success in other complex domains
ML/AI is
well suited for
healthcare &
therapy
development

6. But what are the pitfalls?
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Need more (labelled) data
And healthcare data needs
to be handled carefully
May require specialised
computation & skills
Some problems difficult to
adapt to ML
Bias & interpretability
– data never lies, but
what is it telling us?

7. Radiology & imaging widely used in healthcare
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• X-rays, CT, MRI, PET, sonograms …
• But interpretation is laborious
• Scope for human error
– 71% of detected lung cancers were
retrospectively found on previous scans
– 5-9% disagreement between experts
– 23% when no clinical information
supplied
• Not enough radiologists
• Not enough time
https://www.rsna.org/en/news/2019/
May/uk-radiology-shortage

8. Ai is good at recognising things in images
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• Lots of prior art
• Lots of data to train models
from
• “AI radiologist”
– would be more consistent
– faster
– could double-check or
triage
• But there’s more …

9. Baseline scan Sequential scans
Specific scientific questions to address:
• Can we predict response to specific drugs from the baseline scan? i.e. duration of PFS or OS
• Can we define novel efficacy endpoints? i.e. identify quantitative changes in the image that predict overall
survival more robustly than conventional endpoints (e.g. RECIST)
• Can we get insight into toxicity? i.e. improved prediction, diagnosis or understanding of AEs such as ILD
• Can the scans provide other insights? e.g. tumour genetics, e.g. therapy resistance, e.g. POM biomarkers?
• Can we effectively combine radiomic insights with other clinical data in order to accelerate and
improve patient stratification algorithms?
Radiomic analysis of medical images
Radiomics is the science of extracting quantitative
features from medical images to measure shape,
intensity, density, texture, etc. The analysis of these
‘radiomic features’ can reveal disease characteristics
that are not readily appreciated by the naked eye.

10. AI for PD-L1 scoring in Urothelial Carcinoma
Deep learning can automatically score PD-L1 expression in Tumour cells and
Immune cells
Slide stained for PD-L1 expression Cells that were automatically detected using AI

11. AstraZeneca generates and has access to more data than ever before.
Target ID
Target
Validation
Discovery Pre-Clinical Clinical Commercial
Post
Marketing
Surveillance
Genetic &
Genomic Data
Patient-Centric
Data
Sensors &
Smart Devices
Interactive
Media
Healthcare Information
network
Market
Data

12. “AI will not replace
drug hunters, but drug
hunters who don’t use
AI will be replaced by
those who do.”
-Andrew Hopkins, CEO Exscientia
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Name
20

13. AI for drug candidate selection & prioritization
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https://www.biopharma-excellence.com/news/2019/6/30/artificial-intelligence-a-revolution-in-
biopharmaceutical-development

14. • Similar patient presentation can
mask vastly different molecular
machinery
• Even within a “homogenous”
condition, patients will have
different outcomes
• What are the treatment effects for
individual patients?
Understanding these leads to:
• More effective trials
• More effective treatment
• Insights on pathophysiology
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Patients are heterogenous
Heterogeneity in lesion change in colorectal cancer
Nikodemiou et al. (2020)

15. AI enabled mining of electronic health records to better
understand diseases
COPD T2D
 Transform patients into sequences of diagnosis
codes
 Look for over-represented temporal pairs of codes
 Collapse pairs into trajectories of diagnoses
 Combine similar trajectories with graph similarity
Brunak et al. Nature Coms. 2016
Topology based Patient-Patient network, identify
distinct subtypes of T2D
Dudley et al. Sci. transl. Med, 2015

16. Data driven KOL identification and site selection
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Network Analysis Federated EHRs
Real Time I/E analysis of Trial protocol
Patient referral network of
oncologists & surgeons
treating NSCLC based on
claims data.
Color represents physician
grouping.
Size of bubble represents
physician PageRank.
• Claims data is used to
map physician networks
based on patient
referrals
• Network analytics such
as PageRank algorithm
are used to determine
which physicians are
most important in the
network
• Network connections are
used to map existing
relationships between
oncologists & surgeons

17. Building a external control arm from Real World Data
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Patients with unmet
medical need
Single-arm trial
Inclusion /
exclusion criteria
Matched patients on standard of
care can be compared to new
treatment
Access to New Medicine
Patients from historical
trials / RWE data
Inclusion /
exclusion criteria
Apply Propensity Score Matching
Matching requires Deep data
not just Big Data