The Artificially Intelligent Pharma & Healthcare Sector€¦ · The Artificially Intelligent Pharma...
Transcript of The Artificially Intelligent Pharma & Healthcare Sector€¦ · The Artificially Intelligent Pharma...
M. Morris Hosseini, MSc, PhD Senior PartnerCC Pharma & HealthcareRoland Berger
Grand Hyatt Athens, September 24th 2018
The ArtificiallyIntelligent Pharma & HealthcareSector
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What are the therapies of the future in the digital health era for Pharma and Healthcare and why is Artificial Intelligence so crucial?
How does Artificial Intelligence work and where can it help in leveraging and expanding our existing knowledge pool in Pharma and Healthcare?
How will Artificial Intelligence affect the stakeholder landscape in Pharma and Healthcare and which opportunities and threats arise?
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Population shift along advancing medicine in Pharma and Healthcare
"Individualized""One size fits all"
blockbuster medicine
Per
sona
lizat
ion
focu
s
Past FuturePresent
Blockbuster
Stratified
Precision "P4"- Predictive
- Preventive
- Participatory
- Personalized
Untreatable
precision "P4" medicine
Digital Health as accelerator
Co-diagnostics as accelerator
Pill
Pill
Test Pill
Test
Data
Source: L. Hood, Roland Berger
Modern medicine can reach an ever larger share of the
population, however ever smaller populations
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An enormous amount of health-related data becomes
available but needs to be interpreted for modern medicine
Digital Health data sources and according application opportunities
Data
interpretation
Data generation technologies (Digital Health Data Sources)
Cyto-mics
Digital Health Enabled/Enhanced applications
> Allogeneic Stem Cells
> iPS1)
> CRISPR2)-Cas9
> Advanced imaging
> In-situ hybridization
> Intracellular transport visualization
> Microbiome-genomics
> IVD3)/wearables
> Micro-array sensors
> uHTS4)
> Mass spectroscopy
> Genome sequencing
> Epigenomic profiling
> Transcription mapping
Histo-mics
Microbio-mics
Metabolo-mics
Proteo-mics
Geno-mics
Monitoring of health state / Maintenance of wellbeing
Identification of disease related agents and patterns
Prediction of diseases
Novel therapies and transport mechanisms
Identification of cell differentiation pathways
Regenerative therapies / Gene therapies
Source: Roland Berger 1) induced Pluripotent Stem Cells 2) Clustered Regularly Interspaced Short Palindromic Repeats 3) In Vitro Diagnostics 4) ultra High Throughput Screening
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AI is the core technology to help manage complexity of systems biology to create actionable solutions Complexity of Digital Health in systems biology
Complexity and variability of humans
Organs…
Molecules
Genes
100 trillion cells
35,000 orfs
6 bn nucleotides
20,000 proteins
Cells
Microbiome
Cytome
Metabolome
Proteome
Transciptome
Epigenome
Genome
Multi-Omics Major challenges due to digital health complexity
Organisms Populations
50 organs
> To find relevant signals within this enormous amount of
individual data and enhance the signal-to-noise ratio
> To analyze and interpret the data signals and enable
actionable health related decisions
✓
Source: Roland Berger
Artificial Intelligence as core technology to alleviate complexity challenge
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There is a big 'buzz' around AI in healthcare, which
attracts approx. 18% of global AI investment
Financial Services
Retail
20%
45%
17%
Others
HealthCare
18%
Artificial Intelligence represents one of technology's most important priorities and healthcare is perhaps AI's most urgent application.
— Peter Lee,
Director of Research
I believe we will reach a point around 2029 when medical technologies will add one additional year every year to your life expectancy
— Ray Kurzweil,
Chief Futurist
Source: IDC, Roland Berger
Share of global investment in AI by major industry
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A myriad of AI startups have emerged in the Pharma and
Healthcare space along a great variety of use cases
Source: IDC, CBInsights, Roland Berger
Landscape of AI startups in Pharma and Healthcare
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What are the therapies of the future in the digital health era for Pharma and Healthcare and why is Artificial Intelligence so crucial?
How does Artificial Intelligence work and where can it help in leveraging and expanding our existing knowledge pool in Pharma and Healthcare?
How will Artificial Intelligence affect the stakeholder landscape in Pharma and Healthcare and which opportunities and threats arise?
918_09_24 Economist Presentation AI in Pharma Hosseini fv.pptx
Output data/ answers
AI does not need a defined algorithm –It "creates" one based on enormous amounts of dataComparison between classical programming and AI
Classic pro-gramming
"Smart heuristics"
> Fixed "if this, than that" algorithms are developed during program design
> Algorithm is designed for a specific pattern in input data
> All heuristics need to be specifically considered during design
Machine learning
"AI"
> Machine learning "generates" the algorithm based on large input data sets – the more data, the better the algorithm
> The algorithm adapts with feedback from output data ("the network is trained")
Pre-defined
algorithm
Input data
Self –
learning
algorithm
Feedback
output data
Input data Output data
Source: Roland Berger
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Evidence-based medicine ensures that clinical decisions are made based on what is knownEvidence-based decision making in clinical practice (1/2)
known knowns
known unknowns
unknown unknowns
unknown knowns
Source: Acta Inform Med, NIH, D. Rumsfeld , Roland Berger
> Evidence based medicine (EBM) is the conscientious, explicit, judicious and reasonable use of modern, best evidence in making decisions about the care of individual patients
Evidence-Based Medicine
> EBM integrates clinical experience and patient values with the best available research information
> EBM aims to increase the use of high quality clinical research in clinical decision making
Quadrants ofmedical knowledge
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> Leverage of already existingbut hitherto untappedexperience base
unknown knowns
AI can help us both for the unknown knowns as well
as for the known unknowns with its adaptive algorithms
InformedTreatments
known knowns> Routine anamnesis> Readily accessible knowlegde> Experience pool of GP doctor
> Treatment guidelines> New publications> Rare specific/orphan cases
> Full leverage of currentknowledge base
unknown unknowns
Trial & Error/Research
> Serendipity-driven unexpected experiences
Routinely access-ible and leveraged GP knowlegde
Advanced Specialist medical expertise
"Smart Heuristics"
Artificial Intelligence
Do
ctor K
no
wled
ge
Do
cto
r In
tuit
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known unknowns> Hypothesis-driven non-clinical
and clinical research and development
> AI-powered high-throughput screening and systems biology
Targ
eted exp
ansio
n o
f kno
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and
mech
anistic u
nd
erstand
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Un
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surp
rise d
iscoveries
Source: Roland Berger
Artificial Intelligence leverage points along the quadrants of medical knowledge
unknown knowns
Artificial Intelligence enabled leverage points along quadrants of medical knowledge
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✓ Problem
> Every year about 5.4 million new skin cancer cases in the US; rate of survival decreases from 97% to 14% if detected in a later stage
Approach
> The technology is fueled by deep learning programs and a 130,000 image database of high-quality and pre-diagnosed medical imagery
> The AI is build up on Google's already present AI that was trained to identify 1.28 million images from 1,000 object categories
Advantage
> Technology achieved the accuracy of board-certified dermatologists
> Future goal is an app that can be used as a scanner on human skin lesions to detect skin cancer
Source: Stanford News, Roland Berger
AI-Example: KNOWN UNKNOWNS
Stanford's researchers developed an AI that can detect skin cancer after machine learning with 130,000 imagesImproved skin cancer detection employing AI
Functionality: Visual Processing
> AI powered pattern recognition employing deep learning and pre-diagnosed image database
Source: Stanford News: Roland Berger
1318_09_24 Economist Presentation AI in Pharma Hosseini fv.pptxSource: Company information, Roland Berger
> Recursion uses a combination of artificial intelligence, automation and experimental biology to industrialize the discovery of new cures
> Thousands of drug candidates are tested with different cellular models for rare diseases
> By using AI and advanced automation, large datasets are compiled from cellular images
> Cellular image datasets are used to construct a large portfolio of high-value cellular models that provide insight into disease mechanisms and toxicity
Recursion pharmaceuticals is employing AI for pharma research by automated testing at cellular levelDrug discovery via AI enabled speed testing
AI-Example: KNOWN UNKNOWNS
Source: Company websites, Roland Berger
Artificial intelligence unlocks maximum
data from cellular image datasets
Entirely automated approach allows to
achieve the industrialization of
discovery biology
Revealing genetics through the lens of the
Recursion platform can illuminate a map of
human biology
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Beyond knowledge, physical interaction and personal communication are an essential dimension for decisions
known knowns> Routine anamnesis> Readily accessible knowlegde> Experience pool of GP doctor
> Treatment guidelines> New publications> Rare specific/orphan cases
> Full leverage of currentknowledge base
unknown unknowns
> Serendipity-driven unexpected experiences
Informed Treatments Trial & Error / Research
Interaction /Communication > Physical interaction and
personal communication
Routinely access-ible and leveraged GP knowlegde
Advanced Specialist medical expertise
"Smart Heuristics"
Artificial Intelligence
Do
ctor K
no
wled
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Do
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tuit
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Targ
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f kno
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and
mech
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erstand
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Un
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surp
rise d
iscoveries
> Leverage of already existingbut hitherto untappedexperience base
unknown knowns
known unknowns> Hypothesis-driven non-clinical
and clinical research and development
> AI-powered high-throughput screening and systems biology
Source: Roland Berger
Artificial Intelligence leverage points along the quadrants of medical knowledge
unknown knowns
Artificial Intelligence enabled leverage points along quadrants of medical knowledge
1518_09_24 Economist Presentation AI in Pharma Hosseini fv.pptx
Also along this dimension, AI can help improve clinical decision
making by empowering patients via access to knowledge
known knowns> Routine anamnesis> Readily accessible knowlegde> Experience pool of GP doctor
> Treatment guidelines> New publications> Rare specific/orphan cases
> Full leverage of currentknowledge base
Informed Treatments Trial & Error / Research
Interaction /Communication > Physical interaction and
personal communication
> AI-powered symptom checkers and chatbots
Routinely access-ible and leveraged GP knowlegde
Advanced Specialist medical expertise
"Smart Heuristics"
Artificial Intelligence
Do
ctor K
no
wled
ge
Do
cto
r In
tuit
ion
unknown unknowns
> Serendipity-driven unexpected experiences
Targ
eted exp
ansio
n o
f kno
wled
ge
and
mech
anistic u
nd
erstand
ing
Un
inten
ded
surp
rise d
iscoveries
known unknowns> Hypothesis-driven non-clinical
and clinical research and development
> AI-powered high-throughput screening and systems biology
> Leverage of already existingbut hitherto untappedexperience base
unknown knowns
Source: Roland Berger
Artificial Intelligence leverage points along the quadrants of medical knowledge
unknown knowns
Artificial Intelligence enabled leverage points along quadrants of medical knowledge
1618_09_24 Economist Presentation AI in Pharma Hosseini fv.pptxSource: Company websites, Roland Berger
> Treato collects and analyzes content of patients and caregivers about treatment-related experiences
> Patients not just do research on health-related topics – they also tell their story
> The patented analytics and big data technology turn billions of online conversations into meaningful social intelligence
> Company has partnered with 13 of the top 50 pharmaceutical companies and its website helps millions of visitors each month
> Ada Health is a mobile app which aims to provide a "physician in your pocket"
> The technology employs Artificial Intelligence in combination with medical insights of physicians and hence offers new levels of personalized care
> Recent announcement of a € 40 m private funding
Des
crip
tion
& F
eatu
res
Ada and treato are two digital solutions to support interaction with doctors fostering empowered patientsPatient-supporting apps for symptom checking and treatment advice powered by AI
AI-Example: INTERACTION & COMMUNICATION
Source: Company websites, Roland Berger
1718_09_24 Economist Presentation AI in Pharma Hosseini fv.pptx
What are the therapies of the future in the digital health era for Pharma and Healthcare and why is Artificial Intelligence so crucial?
How does Artificial Intelligence work and where can it help in leveraging and expanding our existing knowledge pool in Pharma and Healthcare?
How will Artificial Intelligence affect the stakeholder landscape in Pharma and Healthcare and which opportunities and threats arise?
1818_09_24 Economist Presentation AI in Pharma Hosseini fv.pptx
100%
100%
Shift in depth of value add in diagnosis and therapy
Current distribution on depth of value add
> Medical practitioners analyze diagnostic test results, conduct patientcounsellations and recommend therapies
Future distribution on depth of value add
> Shift of decision-making from medical practitioners towards algorithms, whichwill conduct diagnoses and derive therapeutic recommendations
> Medical practitioners will increasingly perform QA and provide second and thirdlevel expert support, while main depth of value add is performed by algrorithms
Player movement towards outpatient care
Patient empowerment due to full integration and digitization along patient journey
Hospitals-
HMOs
MedTech
Outpatient
Care
SHIs
PHIs
Startups
Pharma
IT-Players
DIG
ITA
L
Early
detection
Symptoms
Self-diagnoses and per telemedicineAppointments via app
Continuous remote care via sensors and apps
Intelligent, IT-based diagnoses and therapy recommendations
Distribution to the patient
Data / EHR
TrackerTransmission
of patient data
Patient record with data in control of the patient
Direct reimbursement after data transfer to health insurance
AI will shift the asymmetry of knowledge towards patients thereby setting the landscape in motionShift of decision making along stakeholder landscape in healthcare
Medical practitioners
Other healthcare players
Source: Roland Berger
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Danger is not "Artificial Intelligence"– but "Natural Stupidity"
Artificial Intelligence: Threat or Opportunity?
AI
Source picture hair: Wikipedia [email protected]
2018_09_24 Economist Presentation AI in Pharma Hosseini fv.pptx
SENIOR PARTNER
Roland BergerCompetence Center Pharma & HealthcareBertolt-Brecht-Platz 3 | 10117 Berlin | Germany
E-Mail: [email protected]
M. Morris Hosseini, MSc PhD
Your contact for further information
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