Radixweb has released its 2026 Global AI in Healthcare Report, revealing a significant disconnect between artificial intelligence adoption and workforce preparedness in the healthcare sector. The comprehensive study, based on insights from over 750 healthcare professionals worldwide, found that 85% of clinicians feel they need more training to effectively utilize AI tools in both patient care and operational workflows.
The report indicates that healthcare has entered an AI-integrated phase, with 100% of surveyed organizations using AI in some form. Half of healthcare operations currently employ AI for efficiency-driven tasks like scheduling, revenue cycle management, documentation, and automation, with noticeable improvements reported in workflows and patient care. However, this technological progress is being hampered by human readiness challenges.
"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." The training gap introduces particular risk in environments where AI recommendations directly influence patient care decisions.
Beyond workforce training, the report identifies additional barriers to AI implementation. System integration presents a major challenge, with 66% of healthcare IT leaders citing fragmented legacy systems and complex regulatory environments as limitations to seamless AI workflow integration. Value realization also remains problematic, as fewer than half (42%) of organizations have achieved significant returns on their AI investments despite early efficiency improvements.
"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 report positions 2026 as a crucial transition year from AI-assisted workflows to fully integrated AI systems in healthcare.
The study provides detailed performance metrics, showing that 57% of clinicians report stronger clinical decision-making with AI assistance, while 43% observe early reductions in clinical errors. On the development side, large language models lead healthcare AI initiatives, used by 60% of developers, though 57% of developers rank privacy and security as their top concern. The complete findings are available in the 2026 Global AI in Healthcare Report.
This report matters because it highlights a critical bottleneck in healthcare innovation: while AI technology advances rapidly, the human element of implementation lags behind. The training gap identified could slow the realization of AI's potential benefits in patient care, operational efficiency, and cost reduction across global healthcare systems. As healthcare organizations move beyond pilot programs to full-scale AI integration, addressing workforce readiness becomes essential for ensuring patient safety, maximizing return on investment, and maintaining clinical trust in increasingly automated healthcare environments.
