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The findings, interpretations and conclusions expressed herein are a result of a collaborative process facilitated and endorsed by the World Economic Forum but whose results do not necessarily represent the views of the World Economic Forum, nor the entirety of its Members, Partners or other stakeholders.\n© 2025 World Economic Forum. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, including photocopying and recording, or by any information storage and retrieval system.", "attrs": null, "canon": { "kind": { "name": "Disclaimer", "slug": "disclaimer", "status": "active", "canonId": "91173477-d91f-4024-bbaf-d4f93e3f82dc", "version": 1, "description": "Legal/cautionary text that occupies most of the body of its slide." }, "tool": null, "subkind": null, "framework": null }, "subkind": null, "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 18:59:31.088+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "e9bd666c-dda9-4478-b735-caf43b7aa02b", "parentComponentId": null }, { "bbox": { "h": 0.405, "w": 0.535, "x": 0.239, "y": 0.141 }, "kind": "list", "text": "Reading guide 3\nForeword 4\nExecutive summary 5\n1 Context: AI in the health sector 7\n1.1 Trends and use cases 7\n1.2 Industry adoption 10\n1.3 Key barriers 11\n2 Future outlook: Four visions for AI-enabled health 13\n3 Analysis: Three key challenges to scaling AI in health 15\n3.1 Complexity for policy-makers and leaders 15\n3.2 Misalignment of technical choices with strategic visions 18\n3.3 Low confidence in a fragmented governance and regulation framework 20\n4 Strategy: Six calls to action to drive value creation 23\nConclusion 25\nContributors 26\nEndnotes 28", "attrs": null, "canon": { "kind": { "name": "List", "slug": "list", "status": "active", "canonId": "019de52c-f94b-779b-a53b-fd4904a5f4d2", "version": 1, "description": "Bullet or numbered list." }, "tool": null, "subkind": { "name": "Bullet list", "slug": "bullet", "status": "active", "canonId": "019de52c-fc98-7169-a083-5cab805076ca", "version": 1, "description": null }, "framework": null }, "subkind": "bullet", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 18:59:31.088+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "4d3d99f4-ba1c-4c30-8fa3-021177f7d0d9", "parentComponentId": null }, { "bbox": { "h": 0.012, "w": 0.165, "x": 0.239, "y": 0.032 }, "kind": "source-note", "text": "Images: Midjourney, Getty Images", "attrs": null, "canon": { "kind": { "name": "Source note", "slug": "source-note", "status": "active", "canonId": "23484952-494b-4a5e-b1ad-550d0d70e948", "version": 1, "description": "Attribution / source / caveat placed at the slide footer. 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Recognizing that ongoing shifts and advancements are already in motion, the aim is to continuously deepen and update the understanding of AI's implications and applications through collaboration with the community of World Economic Forum partners and stakeholders engaged in AI strategy and implementation across organizations. Together, these papers offer a comprehensive view of AI's current development and adoption, as well as a view of its future potential impact. 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Complexity of AI in health deterring policy-makers and business leaders: Despite the perception that AI in health receives significant attention and energy, it struggles to gain traction on political and strategic agendas. 2. Misalignment of technical choices with strategic visions: Health leaders often delegate technical decisions, missing opportunities to align technology with their strategic goals. Additionally, misaligned incentives often hinder decisions from supporting shared goals and collective ideals. 3. 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At the front end, AI-enabled tools can improve care, from enhancing clinicians' capabilities and freeing up time for them to focus on patient relationships to enabling patients to take greater control of their own health; at the back end, augmenting non-patient-facing elements improves the efficiency of all facets of healthcare delivery and life sciences by optimizing system-wide operations. However, the adoption of AI at scale remains slow, and there is a risk that its transformative potential in healthcare may not be fully realized. The World Economic Forum Digital Healthcare Transformation (DHT) initiative has conducted research on AI adoption in healthcare through interviews with experts. 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However, this is a dangerous pattern, given how some technical decisions limit or constrain the ability to deliver on a vision.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 18:59:34.793+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "2b145aa3-8b31-4526-8879-b7d2fc8ee26c", "parentComponentId": null }, { "bbox": { "h": 0.08, "w": 0.35, "x": 0.23, "y": 0.36 }, "kind": "paragraph", "text": "This vision should incorporate health needs into broader digital public infrastructure (DPI) strategies to ensure that stakeholders (1) do not attempt to \"reinvent the wheel\" and (2) secure equitable access to AI-driven health solutions.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 18:59:34.793+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "7ae2fe53-ff48-46b9-a73d-3296bddac432", "parentComponentId": null }, { "bbox": { "h": 0.06, "w": 0.35, "x": 0.23, "y": 0.81 }, "kind": "paragraph", "text": "Equity must remain at the forefront of AI initiatives in health, and efforts should focus on ensuring that AI tools increase access for those most in need, bridging the gap between populations with and those without quality health services.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 18:59:34.793+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "7b2f607a-c81c-4635-9bdb-5fbb55dd5ca3", "parentComponentId": null }, { "bbox": { "h": 0.12, "w": 0.35, "x": 0.23, "y": 0.45 }, "kind": "paragraph", "text": "Indeed, the persistent access gap to digital health solutions due to inadequate DPI further exacerbates the challenge. For example, focusing on comprehensive datasets that include social determinants of health (SDOH) can help address biases and privacy issues in AI systems.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 18:59:34.793+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "8345362e-bf5d-43b7-8961-b922b56fc668", "parentComponentId": null }, { "bbox": { "h": 0.18, "w": 0.35, "x": 0.6, "y": 0.22 }, "kind": "paragraph", "text": "Successful examples of addressing data ownership and DPI gaps include the United Kingdom's NHS and Israel's health sector. In the United Kingdom, the NHS centralizes public and private health data to enhance transparency and benchmarking, while Israel's centralized data management system played a crucial role in its successful COVID-19 vaccination rollout. 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Establishing flexible regulatory frameworks, integrating human oversight and ensuring transparency are essential to addressing public concerns and building confidence in AI technologies. Both public and private stakeholders have a role in developing adaptable guidelines and standards that keep pace with AI advances. Additionally, promoting data-sharing while protecting privacy and encouraging sustainable business models can enhance AI's impact on health. 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