Thinking about the Impact of Artificial Intelligence on U.S. Health Care Costs and Spending Growth.

The Value Opportunity from Artificial Intelligence in U.S. Health Care Spending.

The analysis considered the savings potential from full national implementation of three specific AI technologies - machine learning (ML), natural language processing (NLP), and generative AI (genAI) - across administrative and medical expense categories for payers and providers (but excluded onetime implementation costs).

A lesson from the past and the future of artificial intelligence in medical decision-making.

Governing the attention dividend: AI-reclaimed clinician capacity as a health policy resource

Abstract Ambient artificial intelligence (AI) is reducing documentation burden in primary care, with real but modest and variable effects: reclaimed minutes in some settings, reduced cognitive load in others, and no guarantee that either reaches patients. This article argues that the resulting capacity—the attention dividend—should be governed as a health policy resource. Without deliberate allocation, it defaults to throughput, administrative absorption, and already-advantaged patients. The article specifies the payment and care model conditions that make deliberate reallocation more feasible, including hybrid and value-based payment, continuity add-on payments, monthly per-patient care-management payments, and primary care spending floors; proposes 3 priority uses—recognition of overlooked patients, continuity, and safety-netting and reassurance; and pairs 8 allocation questions with measurable indicators, concrete policy and operational mechanisms, and accountable actors across health system leaders, payers, purchasers, primary care practices, AI vendors, regulators, and accreditors. Seven named capture mechanisms describe how the dividend fails to reach patients, each with a corrective governance response. The policy question is not whether ambient AI saves time, but whether health systems govern the capacity it returns—through mechanisms that can be named, measured, and assigned.

The role of artificial intelligence in transforming clinical practice and biomedical research: a review of opportunities and challenges

Artificial Intelligence (AI) is rapidly transforming healthcare sector by improving diagnostic accuracy, enhancing drug discovery and pharmaceutical research, and advancing electronic prescription processing (e-prescribing; eRx), although its adoption in developing contexts remains limited. This study the role of artificial intelligence in transforming clinical practice and biomedical research: a review of opportunities and challenges presents systematic review of 20 peer-reviewed articles examining the role of AI in clinical practice and biomedical research. The key findings show that AI significantly improves diagnostic precision, supports personalized medicine, enhances real-time patient management system, and optimizes medication management through intelligent e-prescribing systems with accuracy of perception of drugs for each patient. Additionally, AI contributes to faster and more efficient biomedical research processes. However, challenges such as inadequate infrastructure, high implementation costs, data privacy concerns, and limited technical expertise persist, particularly in developing country. The review concludes that effective and sustainable AI integration in healthcare requires context-specific strategies, supportive policy frameworks, increased investment in digital infrastructure, and capacity building among healthcare professionals.

The Economic Impact of AI Adoption in Healthcare: Estimating National Cost Savings, Productivity Gains, and Long-Term Health Outcomes

It is argued that AI can generate substantial national value when deployed in high-volume, high-cost, and prevention-oriented services and offers policymakers a practical basis for prioritizing responsible AI investments that improve both healthcare efficiency and long-term patient outcomes.

Beyond the Algorithm: Artificial Intelligence, Clinical Decision-Making, and the Moral Ecology of Care in Africa

Artificial intelligence (AI) is increasingly promoted as a tool to enhance clinical decision-making and thus improve the quality of healthcare. While much of the emerging scholarship on AI and healthcare in Africa has focused broadly on opportunities and systemic challenges, what remains underexplored is the specific application of AI to clinical decision-making. This paper contributes to addressing this gap by offering a conceptual and critical analysis of AI-based clinical decision support systems (AI-CDSS) in African contexts. Drawing on philosophical accounts of medical reasoning and relational moral frameworks such as Ubuntu, the paper draws on the moral ecology of care and shows that algorithmic systems can reconfigure epistemic authority, redistribute responsibility, and risk marginalising context-sensitive and relational dimensions of care. The paper further argues that AI systems are better understood as socio-technical mirrors that reflect and amplify existing human values, institutional arrangements, and power asymmetries. Moving beyond the algorithm, it proposes a shift toward relational and context-sensitive AI governance, including the development of relational impact assessments, the redistribution of responsibility across the AI lifecycle, and the co-production of knowledge with local stakeholders. While focusing on African clinical contexts, the analysis offers broader insights for global debates on AI ethics and clinical decision-making.