SLIDE 1
- M. Morris Hosseini, MSc, PhD
Senior Partner CC Pharma & Healthcare Roland Berger
Grand Hyatt Athens, September 24th 2018
2 18_09_24 Economist Presentation AI in Pharma Hosseini fv.pptx
The Artificially Intelligent Pharma & Healthcare Sector M. - - PowerPoint PPT Presentation
The Artificially Intelligent Pharma & Healthcare Sector M. - - PowerPoint PPT Presentation
The Artificially Intelligent Pharma & Healthcare Sector M. Morris Hosseini, MSc, PhD Senior Partner CC Pharma & Healthcare Roland Berger Grand Hyatt Athens, September 24 th 2018 What are the therapies of the future in the digital
SLIDE 2
SLIDE 3
3 18_09_24 Economist Presentation AI in Pharma Hosseini fv.pptx
Population shift along advancing medicine in Pharma and Healthcare
"Individualized" "One size fits all"
blockbuster medicine
Personalization focus
Past Future Present
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
SLIDE 4
4 18_09_24 Economist Presentation AI in Pharma Hosseini fv.pptx
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
SLIDE 5
5 18_09_24 Economist Presentation AI in Pharma Hosseini fv.pptx
AI is the core technology to help manage complexity
- f 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
SLIDE 6
6 18_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
Health Care
18%
Artificial Intelligence represents
- ne 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
SLIDE 7
7 18_09_24 Economist Presentation AI in Pharma Hosseini fv.pptx
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
SLIDE 8
8 18_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?
SLIDE 9
9 18_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 data
Comparison 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
- utput data
Input data Output data
Source: Roland Berger
SLIDE 10
10 18_09_24 Economist Presentation AI in Pharma Hosseini fv.pptx
Evidence-based medicine ensures that clinical decisions are made based on what is known
Evidence-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 of medical knowledge
SLIDE 11
11 18_09_24 Economist Presentation AI in Pharma Hosseini fv.pptx
> Leverage of already existing but hitherto untapped experience base
unknown knowns
AI can help us both for the unknown knowns as well
as for the known unknowns with its adaptive algorithms
Informed Treatments
known knowns
> Routine anamnesis > Readily accessible knowlegde > Experience pool of GP doctor > Treatment guidelines > New publications > Rare specific/orphan cases > Full leverage of current knowledge 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 Doctor Knowledge Doctor Intuition
known unknowns
> Hypothesis-driven non-clinical and clinical research and development > AI-powered high-throughput screening and systems biology Targeted expansion of knowledge and mechanistic understanding Unintended surprise discoveries
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
SLIDE 12
12 18_09_24 Economist Presentation AI in Pharma Hosseini fv.pptx
✓ 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
- bject 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 images
Improved 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
SLIDE 13
13 18_09_24 Economist Presentation AI in Pharma Hosseini fv.pptx Source: 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 level
Drug 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
SLIDE 14
14 18_09_24 Economist Presentation AI in Pharma Hosseini fv.pptx
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 current knowledge 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 Doctor Knowledge Doctor Intuition Targeted expansion of knowledge and mechanistic understanding Unintended surprise discoveries > Leverage of already existing but hitherto untapped experience 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
SLIDE 15
15 18_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 current knowledge 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 Doctor Knowledge Doctor Intuition
unknown unknowns
> Serendipity-driven unexpected experiences Targeted expansion of knowledge and mechanistic understanding Unintended surprise discoveries
known unknowns
> Hypothesis-driven non-clinical and clinical research and development > AI-powered high-throughput screening and systems biology > Leverage of already existing but hitherto untapped experience 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
SLIDE 16
16 18_09_24 Economist Presentation AI in Pharma Hosseini fv.pptx Source: 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
- f 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
Description & Features
Ada and treato are two digital solutions to support interaction with doctors fostering empowered patients
Patient-supporting apps for symptom checking and treatment advice powered by AI
AI-Example: INTERACTION & COMMUNICATION
Source: Company websites, Roland Berger
SLIDE 17
17 18_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?
SLIDE 18
18 18_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 patient counsellations and recommend therapies
Future distribution on depth of value add
> Shift of decision-making from medical practitioners towards algorithms, which will conduct diagnoses and derive therapeutic recommendations > Medical practitioners will increasingly perform QA and provide second and third level 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
DIGITAL
Early detection Symptoms Self-diagnoses and per telemedicine Appointments via app Continuous remote care via sensors and apps Intelligent, IT-based diagnoses and therapy recommendations Distribution to the patient Data / EHR Tracker Transmission
- f 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 motion
Shift of decision making along stakeholder landscape in healthcare
Medical practitioners Other healthcare players
Source: Roland Berger
SLIDE 19
19 18_09_24 Economist Presentation AI in Pharma Hosseini fv.pptx
Danger is not "Artificial Intelligence" – but "Natural Stupidity"
Artificial Intelligence: Threat or Opportunity?
AI
Source picture hair: Wikipedia MICHAELVADON@MICHAELVADON.COMM
SLIDE 20
20 18_09_24 Economist Presentation AI in Pharma Hosseini fv.pptx
SENIOR PARTNER
Roland Berger
Competence Center Pharma & Healthcare Bertolt-Brecht-Platz 3 | 10117 Berlin | Germany
E-Mail: morris.hosseini@rolandberger.com
- M. Morris Hosseini, MSc PhD
Your contact for further information
SLIDE 21
21 18_09_24 Economist Presentation AI in Pharma Hosseini fv.pptx