AI in healthcare is having a moment, but not for the reasons most people think. The industry has moved past the excitement of flashy demos. CIOs and CDS teams have seen enough pilots that never scaled to last a lifetime. What matters now is technology that works in the real world, safely, and at scale.
That distinction is critical. Life sciences is highly regulated and deeply process driven. Any system that touches patient data, medical content, or regulatory workflows has to operate within those constraints. Compliance, safety, and reliability are not optional features — they are foundations.
The conversation is no longer about what AI can do. It is about what it can do responsibly, repeatably, and in alignment with regulations. At Newpage, we see too many AI programs evaluated purely on features, speed, or hype. The reality is that readiness matters more: clean data, trained teams, aligned workflows, and clear governance make the difference between a tool that falters and one that transforms operations.
Responsible use also requires asking the right questions: How does the system explain its decisions? How is bias mitigated? How is regulatory drift tracked and controlled? In healthcare, these are not abstract considerations — they determine whether an AI project succeeds or fails under real-world conditions.
The best AI programs in life sciences are those where leaders invest early in readiness. Once processes, data, and human oversight are in place, AI stops being experimental and becomes operational. It accelerates delivery, reduces errors, and strengthens decision-making — all without compromising compliance.
In our latest blog, we break down the eight critical considerations CIOs should have before buying AI for healthcare. We look at readiness, compliance, risk, and how to move from pilots to scalable platforms while maintaining accountability.
Read the full article here: What CIOs Need to Know Before Buying AI in Healthcare
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