The Future of AI Enabled Health 2025

Endnotes 1. World Economic Forum & Boston Consulting Group. (2024). Transforming healthcare: Navigating digital health with a value-driven approach. https://www.weforum.org/publications/transforming-healthcare-navigating-digital-health-with-a- value-driven-approach/ 2. Ibid. 3. For example, commercial interests often hinder data collaboration. 4. Fortune Business Insights. (2024). Artificial intelligence (AI) in healthcare market size, share & industry analysis, by platform (solutions and services), by application (robot-assisted surgery, virtual nursing assistant, administrative workflow assistance, clinical trials, diagnostics, and others), by end-user (hospitals & clinics, pharmaceutical & biotechnology companies, contract research organization (cro), and others), and regional forecasts, 2024–2032. https://www. fortunebusinessinsights.com/industry-reports/artificial-intelligence-in-healthcare-market-100534 5. Chandran, P ., Neal, L., McBiren, J., & Quinton, S. (2023, October). Generative AI in health and opportunities for public sector organizations. Boston Consulting Group. https://web-assets.bcg.com/14/12/d1e1c9a543a98908a3f1d3f216d9/ generative-ai-in-health-and-opportunities-without-spine98.pdf 6. Also for medtech. 7. The information industry includes large technology companies such as Google and Microsoft. 8. Goldfarb, A., & Teodoridis, F. (2022, March 9). Why is AI adoption in health care lagging? Brookings. https://www.brookings.edu/articles/why-is-ai-adoption-in-health-care-lagging/ 9. “Hallucinations” occur when large language models (LLMs) perceive patterns or objects that are non-existent, creating inaccurate outputs. 10. This is a term from the pharmaceutical industry that outlines the path from basic science research – e.g. preclinical and animal studies – to clinical studies in humans, at which point a large proportion of promising discoveries fail. 11. See Collier, M., & Fu, R. (2020, July 30). AI – Healthcare’s new nervous system. Accenture. https://www.accenture.com/ au-en/insights/health/artificial-intelligence-healthcare 12. McKinsey & Company. (2022, December 6). The state of AI in 2022 – and a half decade in review. https://www.mckinsey. com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2022-and-a-half-decade-in-review 13. Sahni, N., et al. (2023). The potential impact of artificial intelligence on healthcare spending. National Bureau of Economic Research. https://www.nber.org/system/files/working_papers/w30857/w30857.pdf 14. As emphasized in the Brazil G20 Health Ministerial Declaration (October 31, 2024): “We will also promote the development of digital public infrastructure for health adapted to climatic and environmental conditions.” Health ministers announced the creation of a global coalition for local and regional production of health services and products. https://www.g20.utoronto.ca/2024/241031-health-declaration-climate-equity.html 15. Vijayan, S., et al. (2023) Implementing a chest X-ray artificial intelligence tool to enhance tuberculosis screening in India: Lessons learned. PLOS Digit Health, 2(12): e0000404. https://journals.plos.org/digitalhealth/article?id=10.1371/journal. pdig.0000404 16. NHS England. (2021). Using chest imaging AI to support COVID-19 research and development. https://transform. england.nhs.uk/blogs/using-chest-imaging-ai-support-covid-19-research-and-development/ 17. World Health Organization. (2024). Ethics and governance of artificial Intelligence for health. https://www.who.int/ publications/i/item/9789240084759 18. HealthAI. (2024). Mapping AI governance in health: From global regulatory alignments to LMICs’ policy developments. https://clias.iecs.org.ar/wp-content/uploads/2024/10/HealthAI_GlobalLandscapeReport_Oct.2024.pdf 19. The White House. (2023, October 30). Executive order on the safe, secure and trustworthy development and use of artificial intelligence. https://www.whitehouse.gov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the- safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence/ 20. American National Standards Institute. (2024). Draft standard-driven public-private partnerships (SD-PPPs) models . https://share.ansi.org/Shared%20Documents/Standards%20Activities/Standards-Drive%20Public-Private%20 Partnership%20for%20CETs/Automated%20%26%20Connected%20Infrastructure%20Brainstorming%20 Session_30July2024/PPP%20Models_Draft.pdf 21. Federated learning allows for the analysis and decentralized training of machine-learning models directly on the devices that hold the training data. Unlike traditional machine learning, federated learning does not require training data to be gathered in a central location. Instead, the data remains on the original devices, while the training algorithm is distributed to where the data resides. See Raab, R., et al. (2023, November). Federated electronic health records for the European Health Data Space. Lancet Digital Health, 5(11) E840–E847. https://www.thelancet.com/journals/landig/article/PIIS2589- 7500(23)00156-5/fulltext The Future of AI-Enabled Health: Leading the Way 28