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M. Morris Hosseini, MSc, PhD Senior Partner CC Pharma & Healthcare Roland Berger Grand Hyatt Athens, September 24 th 2018 The Artificially Intelligent Pharma & Healthcare Sector

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Page 1: The Artificially Intelligent Pharma & Healthcare Sector€¦ · The Artificially Intelligent Pharma & Healthcare Sector. 18_09_24 Economist Presentation AI in Pharma Hosseini fv.pptx

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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618_09_24 Economist Presentation AI in Pharma Hosseini fv.pptx

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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818_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?

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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

ion

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

wled

ge

and

mech

anistic u

nd

erstand

ing

Un

inten

ded

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

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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

ge

Do

cto

r In

tuit

ion

Targ

eted exp

ansio

n o

f kno

wled

ge

and

mech

anistic u

nd

erstand

ing

Un

inten

ded

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

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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

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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

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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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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]

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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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