Radixweb has released its 2026 Global AI in Healthcare Report, revealing that 85% of healthcare professionals feel they need additional training to use artificial intelligence effectively in both patient care and operations. This finding highlights a growing gap between rapid AI adoption and workforce readiness as healthcare organizations move beyond pilot programs to embed AI into everyday workflows.
The comprehensive study, based on insights from over 750 professionals including doctors, healthcare IT leaders, and AI developers worldwide, shows that while AI implementation is now universal among surveyed organizations, human readiness has become the limiting factor. "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."
According to the report, 50% of healthcare operations currently use AI for efficiency-driven workflows such as scheduling, revenue cycle management, documentation, and automation, with clinicians and IT leaders reporting noticeable improvements. The data indicates AI is delivering tangible impact, with 57% of clinicians reporting stronger clinical decision-making and 43% seeing early reductions in clinical errors from AI use.
However, the study uncovers significant structural challenges beneath this progress. "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." The 85% training gap introduces particular risk in environments where AI recommendations directly influence patient care decisions.
Beyond training deficiencies, the report identifies system integration and value realization as compounding challenges. According to 66% of healthcare IT leaders, fragmented legacy systems and complex regulatory environments continue to limit AI's ability to move seamlessly across workflows. Additionally, while organizations have noted early efficiency improvements, fewer than half (42%) have realized significant returns on their AI investments, highlighting a disconnect between investment timing and impact realization.
The report also provides insights into technical adoption patterns, revealing that large language models lead healthcare AI development, used by 60% of developers, while 57% of developers rank privacy and security as their top concern. These findings suggest that while technical capabilities are advancing, implementation challenges remain substantial.
For business and technology leaders, the report signals that 2026 represents a crucial transition year from AI-assisted workflows to fully integrated systems. The future progress of healthcare AI depends on addressing workforce skills gaps, developing interoperable infrastructure, and establishing governance models that ensure clinical trust alongside operational growth. The complete findings are available in the 2026 Global AI in Healthcare Report, which serves as a practical guide for organizations navigating this complex integration landscape.
