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FINAL KEYNOTE Revised Presentation Dimitris World AI Show 13 July 2022.pdf
- 1. Green Data for Green AI Is new European Health Data Space Regulation timely? Dr. Dimitris Kalogeropoulos, Independent Advisor, World Bank, WHO, European Commission World AI Show Keynote July 13th 2022
- 2. Agenda Establish a baseline for AI in health Identify shortfalls toward impact investing in AI Suggest a way forward to scale and sustain value in AI
- 3. Baseline 2022 Report Sanford Institute for Human‐ Centered AI Disconcerting gap between corporate recognition of AI risks and efforts to mitigate Investment is off the hook Ethics is centre‐stage Data are a problem Carbon footprint is an issue Diversity is an issue AI vision has reached a plateau Reasoning is still a frontier of AI
- 4. Baseline Council of Europe Steering Committee for Human Rights in Biomedicine & Health AI deployment in care remains nascent Clinical efficacy has been established for few apps compared to reported research activity Performance generalization from trials to clinical practice is largely unproven Begs the question: What are we doing wrong?
- 5. Scaling Spreading Sustaining data recycling We don’t recycle ! design impact clinical economy society 3S policies Data recycling is necessary for 3S policies To make the transition from AI design to impact To unlock value in AI and innovation To implement green health policies
- 6. Sustainability Impact Ratio (SIR) = Health Impact Environmental Impact (cost) Currently experiencing a vast digital acceleration: (1) new diagnostics (2) big data (3) Covid‐19 (4) wearables Hope is AI will help produce value Every new AI design = new data set design Data are farmed in big data pools (cost, emissions) – data of unknown origins in quality predictor value ‐ ground truth performance, cohort bias or errors This data is completely removed from the clinical reality Vicious circle: Scaling not possible -> health impact - value remains unproven -> adverse impacts – opportunity costs silently proliferate Finally, AI is not an innovation without the delivery service – instead is exploited to deliver a new way of doing things
- 7. Restore SIR • A Green-AI-for-Green- Health-and-Sustainable- Innovation-Policy is long overdue • Seek to restore the balance in the innovation ecosystem with data recycling, and to enable health impact scaling and digital acceleration spillovers • Recycled data must adhere to strict reuse standards (ex- interoperability) and will therefore be reliable and accurate, further reducing carbon emissions: with higher AI value, efficiencies and better productivity
- 8. Reduce carbon, not add ! Healthcare already has significant contribution to the global carbon footprint, that is before we consider Data overproduction related emissions The AI production efficiency impact on the climate or data farming The low AI‐exploiting innovation SIR
- 9. Specifically Combat these Carbon Sources Compromised value in core ecosystem innovation farms Industry‐led designs and outcomes Over- or Under Diagnosis Low or no-value Care Over- medicali sation AMR Downstream -focused policies Treatments with poor health outcomes - high climate impact Patient-in- wrong-cohort decisions High risk-cost, low benefit treatments Overuse of measurement technology Reduction in efficiency of health systems Patient care pathway fragments Big data Siloed care and data Passive Core HCS Over- or Under Use Introvert Implosive Remote triaging AI Radiology image screening AI
- 10. Carbon Reducing Innovation Helix Data ecosystem Restoration ecosystem restoration SDGs, 3 Billion targets, digital diplomacy Value- based Care Quaternary Prevention Improved, faster, more relevant clinical trials Upstream and prevention focused policies Digital Designs for high-value care pathways Re-admission prevention virtual care Patient engagement & participation Precision Diagnosis Data Recycling Green Data Loosely, seamlessly connected Innovation Remote care Shared Results – Common Data Sources Active Core Extrovert Scalable Precision Treatment
- 14. Objectives Intervene in High Carbon Emission Areas to produce and recycle green data and fuel Good AI Introduce Digital Asset Passports to meet recycling standards Mandate reporting on current practice emissions e.g. readmissions per disease per value pathway Reduce carbon emissions in current practices adopting green health policies and enabling recycled data Reduce amount of data generated with computers and medical equipment Introduce circular economy models in innovation Establish new digitally enabled green care models e.g. quaternary prevention Reduce data farming
- 15. Greener Health Approach New Policy Paradigms Agile Data Ecosystem Restoration Public- Social- Private Partnership Green Digital Accelerators Enable 3C AI Mass Green acceleration Greener Health Green Digital Health Meta-Policy weaves together directionality components: 1. Governance: Social- Data-Clinical-Economic 2. Data Tech: Ecosystem Decentralisation- Semantic Federation 3. Telehealth: Access- Scale-Engage- Participate 4. Agile migration
- 18. INNOVATION 6 ‐ Realize Green, Digital, Just Transition: Prioritize innovations that catalyse the transition to a green, digital and just economy for sustainable development IMPACT INITIATIVE 2.6 – Initiate the design and development of standards and pilot testing for digital product passports that track a product’s climate, environmental, and social impacts throughout the value chain IMPACT INITIATIVE 3.3 – Decentralised Financing Coalition for Digital Environmental Sustainability (CODES) UN CODES Action Plan for a Sustainable Digital Planet ‐ 2022 No mention of a digital acceleration ecosystem
- 19. EU 2022 Twining Green and Digital Strategic Foresight Report Mutual reinforcement of the green and the digital transitions Adoption of circular economy models in all sectors Digital technologies catalyse the green transition Just [inclusive] transition is crucial for widespread acceptance of green digital solutions Social awareness, governance and innovation participation could avoid rebound effects, such as increased consumption as a result of efficiency gains and cost savings Green digital diplomacy Ensure policy cohesion No mention of a digital acceleration ecosystem
- 20. Data Principles for Recycling Value Trust Social Contract Equity The full value of data materializes when systems enable the use and reuse of data for different purposes All share equitably in the benefits of data when investments and regulations create a level playing field A trust environment is created when the rights and interests that all stakeholders have in data are safeguarded Social Contract for Data - World Bank Flagship Report Data for Better Lives
- 21. Recycling enablers Precision Med/ Outcome Innovation Observation Innovation: Telehealth, Systems Med Health or Disease State Diagnosis Shared Goal Treatment Innovation (precision Tx) 1. Digital ID 2. Privacy protection 3. Agent (care) prevalence 4. Cybersecurity 5. Clinical prevalence 6. Collective agency 7. DLT for value supply 8. Digital Existence Graphs 9. Collaborative care 10. Integrated care 11. 3C AI 12. Shared care 13. Seamless care 1 2 3 13
- 22. Roadmap to Data Recycling Recycled Data Core Marker patterns for diagnosis, prognosis or prevention – mapped to standardised core Wider systems medicine or heuristic/ algorithmic model Precision medicine service models (4P) Outcome Observation Health or Disease State Diagnosis Treatment Agile Migration High Carbon Practice Area: Prognosis and precision management of acute ischaemic stroke with classification of sub-type (TOAST). Model feature value tested using EHR data and ML “mining”.
- 23. Action plan for Green Health data recycling adopt green health approach restore healthcare environment restore innovation ecosystem adopt 3S policies for value design for equity inclusion impact Integrated telehealth Digital Acceleration platforms 3S action Impact investing aims actions