Artificial Intelligence In Health And Health Care
AI's Transformative Potential in Health: Navigating Priorities for Safe and Equitable Implementation
The advent of generative AI and deep learning ushers in a new era of opportunity for health and health care. This article, part of the National Academy of Medicine's Vital Directions for Health and Health Care: Priorities for 2025 initiative, outlines four strategic areas crucial for the incoming presidential administration to ensure AI is used safely, effectively, and equitably. These include ensuring trustworthy AI, developing an AI-competent workforce, investing in AI research, and clarifying AI liability and responsibilities. The goal is to harness AI's power to promote the health of all Americans.
Executive Impact
Key metrics demonstrating the potential influence of AI across healthcare sectors.
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Potential Efficiency Gains
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Reduction in Diagnostic Errors
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Healthcare Cost Savings
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Ensuring Trustworthy AI
AI-Competent Workforce
Research Investment
Liability & Responsibility
7%
Initial sepsis prediction tool accuracy, missing 67% of cases.
183/2552
Sepsis patients detected by early AI, out of total not treated timely.
AI System Development & Deployment Lifecycle
Needs Assessment
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Data Collection & Preparation
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Model Development & Training
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Predeployment Validation
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Real-World Deployment
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Continuous Monitoring & Retraining
50%
Physician burnout rate decline, partly due to AI integration potential.
Developing AI Literacy in Medical Curricula
Integrating basic AI knowledge into medical and allied health professional training programs is essential. This includes understanding AI limitations, applications, and ethical considerations. Programs must avoid simply adding requirements and instead focus on conferring essential knowledge for safe, effective, and compassionate care in concert with new AI technologies. For instance, universities partnering with healthcare institutions to offer focused curricula in ethics, equity, computer science, and data science can equip the future workforce to leverage AI effectively.
| Research Area | Current Focus | Future Priorities |
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| Disease Mechanisms |
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| Diagnosis & Screening |
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| Precision Medicine |
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NIH Initiatives: Advancing Health Equity through AI Research
The NIH's Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) and the Bridge to Artificial Intelligence programs exemplify focused research investments. These initiatives aim to advance scientific understanding of disease, improve diagnostic capabilities, and enable precision medicine by leveraging AI. Such programs are critical for addressing understudied areas like mental health and rare diseases, ensuring AI models are developed with diverse data, and integrating AI into clinical workflows effectively.
AI Liability Framework Development
Identify Common Legal Questions
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Promulgate Model Licensing Terms
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Set Model Indemnification Terms
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Ease Responsible AI Adoption
Unmuddled
Current AI liability landscape described as 'muddled', leading to a 'responsibility gap'.
Advanced ROI Calculator
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Estimated Annual Savings
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Annual Hours Reclaimed
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Strategic Implementation Roadmap for AI in Healthcare
A structured approach is vital for integrating AI safely and effectively into health and health care systems. Our roadmap outlines key phases to guide organizations through successful AI adoption.
Phase 1: Readiness Assessment & Governance
Establish clear definitions for health care AI, assess organizational readiness, and develop robust governance policies focusing on transparency, trustworthiness, and equity. Implement 'algorithmovigilance' for continuous monitoring.
Phase 2: Workforce Development & Training
Integrate AI education into medical curricula and allied health training. Support professional societies in establishing AI competencies. Address clinician burnout by optimizing workflows and focusing on essential AI skills.
Phase 3: Targeted Research & Development
Invest in research for safe and effective AI use across medicine, practice, and care delivery. Focus on understanding disease mechanisms, improving diagnostics, and advancing precision medicine. Prioritize data quality, privacy, and explainability.
Phase 4: Policy & Liability Clarity
Clarify legal responsibility and liability for AI use. Develop model licensing and indemnification terms. Incentivize equitable AI deployment and establish payment mechanisms to support lower-resourced organizations.
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