The 2026 Global AI in Healthcare Report from Radixweb reveals a significant disconnect between artificial intelligence adoption and workforce preparedness in healthcare settings. Based on insights from over 750 professionals worldwide, including clinicians, healthcare IT leaders, and AI developers, the report indicates that while 100% of surveyed healthcare organizations use AI in some form, 85% of healthcare professionals feel they need more training to use these tools effectively in patient care and operations.
"Healthcare has clearly entered its AI-integrated phase," said Divyesh Patel, CEO of Radixweb. "What this report makes evident is that technology is no longer the limiting factor. Human readiness is. Clinicians recognize the value of AI, but without structured training and organizational support, that value cannot be fully realized." This training gap emerges as healthcare organizations prepare to move beyond pilot programs and embed AI into everyday workflows, creating potential risks in environments where AI recommendations directly influence patient care.
The report documents substantial AI impact already occurring in healthcare operations, with 50% of organizations using AI for efficiency-driven workflows such as scheduling, revenue cycle management, documentation, and automation. Both clinicians and IT leaders report noticeable improvements in workflows and patient care, with 57% of clinicians reporting stronger clinical decision-making with AI and 43% seeing early reductions in clinical errors. However, these benefits are tempered by structural challenges that could limit AI's potential.
"AI maturity is rising faster than organizational maturity," said Dharmesh Acharya, COO of Radixweb. "We're seeing strong adoption, but scaling responsibly requires more than deployment. It requires investment in skills, governance, and trust across clinical and IT teams." Beyond training deficiencies, the report identifies system integration as a major adoption hurdle, with 66% of healthcare IT leaders citing fragmented legacy systems and complex regulatory environments as limitations to AI's seamless workflow integration.
Value realization presents another significant challenge, as fewer than half (42%) of organizations have realized significant returns on their AI investments despite early efficiency improvements. This highlights a timing disconnect between investments and their impacts. The report also notes that large language models lead healthcare AI development, used by 60% of developers, while 57% of developers rank privacy and security as their top AI concern.
According to the report, 2026 is set to be a crucial year in the evolution of AI in healthcare, marking a transition from AI-assisted workflows to fully integrated systems. The future of this progress hinges on addressing workforce skills gaps, developing interoperable infrastructure, and establishing governance models that ensure clinical trust alongside operational growth. Readers can access more statistics and complete findings in the 2026 Global AI in Healthcare Report.
