In early 2025, the U.S. Department of Health and Human Services (HHS) released a nearly 200-page “AI in Healthcare Strategic Plan”, outlining how the U.S. government intends to accelerate the adoption of AI in the healthcare sector. The plan focuses on four key goals: fostering innovation and application of AI in healthcare, promoting trustworthy AI development, ensuring ethical AI use, and protecting patients. This strategy not only highlights the U.S. government’s recognition of AI’s potential in healthcare but also signals a new wave of transformation in the global AI healthcare market.
Reshaping Healthcare with New Technology
In the HHS strategy, AI in healthcare is not an isolated concept but one that integrates into the entire healthcare ecosystem. The last concept of similar importance was “digital health” a decade ago. As we know, “digital health” drove the industry’s digital transformation, creating a vast and dynamic market and giving rise to many successful companies. Clearly, HHS has high expectations for AI in healthcare. Beyond the belief in AI’s potential, the enthusiasm of markets and investors likely spurred HHS to prioritize AI in healthcare:
The HHS AI strategy is divided into five core areas and two supplementary domains. Each core area aims to achieve the four key goals mentioned above. By breaking these goals into specific tasks and assigning responsibilities, HHS has created a comprehensive action plan to drive AI adoption in healthcare.
Deep Dive into the Healthcare Services Domain
Among the five core areas, healthcare services stand out as a critical and controversial sector, especially in the U.S. Feedback collected by HHS reveals that some healthcare providers, including hospitals, clinics, and doctors, remain cautious about AI’s clinical applications, contrary to the expected rapid embrace of new technology.
Their concerns stem from distrust in AI, as described in the strategic plan:
Predictions for AI Trends in U.S. Healthcare
- Massive and Growing Investments in AI Healthcare
AI healthcare projects account for about 25% of all healthcare venture capital investments, totaling over $19 billion since 2021, with $7 billion in 2023 alone. Preliminary reports show that two-thirds of these investments target clinical applications, while the remaining third focuses on administrative uses.
- Enthusiasm and Concerns Coexist
A survey of 100 healthcare leaders found that over 70% have already or plan to deploy AI in healthcare. However, another survey revealed that about 40% of doctors are excited but also worried. They fear AI could reshape employment in healthcare and potentially harm doctor-patient relationships. In 2022, 60% of patients expressed discomfort with doctors relying on AI.
- Heterogeneity in Data and IT Systems
Differences in data management, clinical and administrative systems, and infrastructure (e.g., cloud computing) hinder AI adoption. Variations in data formats and standards across institutions further complicate cross-institutional AI applications, influencing decision-makers’ choices in AI strategies and partnerships.
- Increased Innovation in Administrative AI
In recent years, AI has made strides in administrative tasks due to lower development costs compared to clinical use cases and advancements in GenAI and LLM technologies. Recent applications include extracting medication names from doctors’ notes, answering patient queries, summarizing medical conversations, and drafting medical histories. According to the American Medical Association, 54% of doctors are excited about using AI for administrative tasks like documentation.
Despite its promising future, AI in healthcare faces significant challenges. Data privacy and security remain major barriers. The lack of transparency and explainability in AI models undermines trust among doctors and patients. Additionally, data silos across institutions limit AI’s effectiveness.
Clearly, the challenges faced by the U.S. in integrating AI into clinical work mirror those in other countries. International collaboration, data sharing, and improving AI transparency can help foster the healthy development of AI in healthcare.
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